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Dissertations / Theses on the topic 'Hyperspectral imaging'
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Porter, Michael Anthony. "Hyperspectral imaging using ultraviolet light /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Dec%5FPorter.pdf.
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Thesis (M.S. in Astronautical Engineering)--Naval Postgraduate School, December 2005.Thesis Advisor(s): Richard C. Olsen, Christopher Brophy. Includes bibliographical references (p.55-56). Also available online.
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Sjunnebo, Joakim. "Hyperspectral imaging for gas detection." Thesis, KTH, Tillämpad fysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-169623.
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Porter, Michael A. "Hyperspectral imaging using ultraviolet light." Thesis, Monterey, California. Naval Postgraduate School, 2005. http://hdl.handle.net/10945/1817.
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The LINEATE IMAGING NEAR ULTRAVIOLET SPECTROMETER (LINUS) instrument has been used to remotely detect and measure sulfur dioxide (SO2). The sensor was calibrated in the lab, with curves of growth created for the 0.29 0.31 æ - spectral range of the LINUS sensor. Field observations were made of a coal burning plant in St. Johnâ s, Arizona at a range of 537 m. The Salt River Coronado plant stacks were emitting on average about 100 ppm and 200 ppm from the left and right stacks respectively. Analysis of the LINUS data matched those values within a few percent. Possible uses for this technology include remote verification of industry emissions and detection of unreported SO2 sources.
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Jones, Julia Craven. "Infrared Hyperspectral Imaging Stokes Polarimeter." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/145409.
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This work presents the design, development, and testing of a field portable imaging spectropolarimeter that operates over the short-wavelength and middle-wavelength portion of the infrared spectrum. The sensor includes a pair of sapphire Wollaston prisms and several high order retarders to produce the first infrared implementation of an imaging Fourier transform spectropolarimeter, providing for the measurement of the complete spectropolarimetric datacube over the passband. The Wollaston prisms serve as a birefringent interferometer with reduced sensitivity to vibration when compared to an unequal path interferometer, such as a Michelson. Polarimetric data are acquired through the use of channeled spectropolarimetry to modulate the spectrum with the Stokes parameter information. The collected interferogram is Fourier filtered and reconstructed to recover the spatially and spectrally varying Stokes vector data across the image.The intent of this dissertation is to provide the reader with a detailed understanding of the steps involved in the development of this infrared hyperspectral imaging polarimeter (IHIP) instrument. First, Chapter 1 provides an overview of the fundamental concepts relevant to this research. These include imaging spectrometers, polarimeters, and spectropolarimeters. A detailed discussion of channeled spectropolarimetry, including a historical study of previous implementations, is also presented. Next a few of the design alternatives that are possible for this work are outlined and discussed in Chapter 2. The configuration that was selected for the IHIP is then presented in detail, including the optical layout, design, and operation. Chapter 3 then presents an artifact reduction technique (ART) that was developed to improve the IHIP's spectropolarimetric reconstructions by reducing errors associated with non-band-limited spectral features. ART is experimentally verified in the infrared using a commercial Fourier transform spectrometer in combination with Yttrium Vanadate as well as Cadmium Sulfide retarders.The remainder of this dissertation then details the testing and analysis of the IHIP instrument. Implementation of ART with the IHIP as well as the employed calibration techniques are described in Chapter 4. Complete calibration of the IHIP includes three distinct processes to provide radiometric, spectral, and polarimetric calibration. With the instrument assembled and calibrated, results and error analyses are presented in Chapter 5. Spectropolarimetric results are obtained in the laboratory as well as outdoors to test the IHIP's real world functionality. The performance of the instrument is also assessed, including experimental measurement of signal-to-noise ratio (SNR), and an analysis of the potential sources of systematic error (such as retarder misalignment and finite polarizer extinction ratio). Chapter 6 presents the design and experimental results for a variable Wollaston prism that can be added to the IHIP to vary the fringe contrast across the field of view. Finally, Chapter 7 includes brief closing remarks summarizing this work and a few observations which may be useful for future infrared imaging Fourier transform channeled spectropolarimeter instruments.
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Hartke, John. "DUAL BAND HYPERSPECTRAL IMAGING SPECTROMETER." Diss., The University of Arizona, 2005. http://hdl.handle.net/10150/195994.
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A temporally and spatially non-scanning imaging spectrometer covering two separate spectral bands in the visible region using computed tomographic imaging techniques is described. The computed tomographic techniques allow for the construction of a three-dimensional hyperspectral data cube (x, y, λ) from the two-dimensional input in a single frame time. A computer generated holographic dispersive grating is used to disperse the incoming light into several diffraction orders on a focal plane composed of interwoven pixels independently sensitive to the two bands of interest. Separating the input of the two spectral pixel types gives co-registered output between the two bands and overcomes the limitation of overlapping orders. The proof of concept in the visible is presented using a commercially available camera.The lessons learned from the visible system are applied to a dual infrared band imaging spectrometer. Utilizing recent developments in dual band infrared focal planes a dual band imaging spectrometer is designed covering portions of the MWIR and LWIR atmospheric transmission windows. The system design includes the evaluation of recent developments in dual band infrared focal planes, the design and evaluation of the computer generated holographic disperser, and the optical elements in the system.
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MAKKI, IHAB. "Hyperspectral Imaging for Landmine Detection." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2700516.
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This PhD thesis aims at investigating the possibility to detect landmines using hyperspectral imaging. Using this technology, we are able to acquire at each pixel of the image spectral data in hundreds of wavelengths. So, at each pixel we obtain a reflectance spectrum that is used as fingerprint to identify the materials in each pixel, and mainly in our project help us to detect the presence of landmines.
The proposed process works as follows: a preconfigured drone (hexarotor or octorotor) will carry the hyperspectral camera. This programmed drone is responsible of flying over the contaminated area in order to take images from a safe distance. Various image processing techniques will be used to treat the image in order to isolate the landmine from the surrounding. Once the presence of a mine or explosives is suspected, an alarm signal is sent to the base station giving information about the type of the mine, its location and the clear path that could be taken by the mine removal team in order to disarm the mine.
This technology has advantages over the actually used techniques:
• It is safer because it limits the need of humans in the searching process and gives the opportunity to the demining team to detect the mines while they are in a safe region.
• It is faster. A larger area could be cleared in a single day by comparison with demining techniques
• This technique can be used to detect at the same time objects other than mines such oil or minerals.
First, a presentation of the problem of landmines that is expanding worldwide referring to some statistics from the UN organizations is provided. In addition, a brief presentation of different types of landmines is shown. Unfortunately, new landmines are well camouflaged and are mainly made of plastic in order to make their detection using metal detectors harder. A summary of all landmine detection techniques is shown to give an idea about the advantages and disadvantages of each technique.
In this work, we give an overview of different projects that worked on the detection of landmines using hyperspectral imaging. We will show the main results achieved in this field and future work to be done in order to make this technology effective.
Moreover, we worked on different target detection algorithms in order to achieve high probability of detection with low false alarm rate. We tested different statistical and linear unmixing based methods. In addition, we introduced the use of radial basis function neural networks in order to detect landmines at subpixel level. A comparative study between different detection methods will be shown in the thesis.
A study of the effect of dimensionality reduction using principal component analysis prior to classification is also provided. The study shows the dependency between the two steps (feature extraction and target detection). The selection of target detection algorithm will define if feature extraction in previous phase is necessary.
A field experiment has been done in order to study how the spectral signature of landmine will change depending on the environment in which the mine is planted. For this, we acquired the spectral signature of 6 types of landmines in different conditions: in Lab where specific source of light is used; in field where mines are covered by grass; and when mines are buried in soil. The results of this experiment are very interesting. The signature of two types of landmines are used in the simulations. They are a database necessary for supervised detection of landmines. Also we extracted some spectral characteristics of landmines that would help us to distinguish mines from background.
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Nguyen, Dinh hoang. "Development of an optical system for preclinical molecular imaging of atherothrombosis." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCD062/document.
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Dans ce travail de thèse, nous développons des protocoles d'imagerie optique pour l'observation des nanoparticules sur des coupes de tissus afin de relier leur localisation et leur «comportement» à l'environnement biologique, en particulier son éventuel état pathologique. Nous avons synthétisé des agents de contraste bimodaux, sous forme de nanoparticules -NP- visibles en résonance magnétique et en optique, à base d'oxydes de fer et de zinc (Zn(Fe)O) avec une nouvelle méthode de polyol azéotropique dans des solvants glycoliques (DEG et PG). L'élimination de l'eau à l'aide de l'appareil Dean-Stark est une nouvelle stratégie pour la synthèse de NP dans une solution de polyol, avec un rendement élevé et produisant des particules de petite taille. Les NP les plus visibles, selon leur contraste IRM, ont été revêtus de carboxyméthyl pullulane, de polyéthylène glycol, de carboxyméthyl dextrane et de fucoïdane, ce dernier étant un polysaccharide capable de se lier spécifiquement à la paroi vasculaire. Les NPs montrent de bonnes propriétés magnétiques et optiques à température ambiante. Les NP recouvertes ont été injectées dans un modèle de rat d’athérothrombose pour localiser le thrombus par IRM avant sacrifice et collecte des tissus pour étude des coupes histologiques par microscopie optique. La différence entre les images IRM avant et après l'injection de fucoïdane-NPs et de CMD-NPs est claire. Les résultats montrent que les NP recouvertes de fucoïdane sont liées au thrombus. Certains types de microscopies, tels que la microscopie de fluorescence, la microscopie en champ sombre, la microscopie hyperspectrale à champ sombre et la microscopie interférentielle à champ sombre ont été développés pour la détection des NPs en milieu liquide et dans les tissus. En analysant le spectre de chaque pixel et en le comparant au spectre des matériaux de référence, la microscopie hyperspectrale peut détecter la présence de NPs sur des coupes de tissus, les localiser, les identifier et les caractériser. Zn(Fe)O NPs constituerait donc un agent de contraste bimodal potentiel pour l’IRM et l’imagerie optique. Cependant, bien que de nombreux outils optiques avancés aient été développés, nous avons constaté qu'il est toujours difficile d'identifier de manière fiable les NP dans le tissuIn this thesis work, we develop optical imaging protocols for the observation of then anoparticles on tissue slices in order to further link their localization and their “behaviour” to the biological pathological environment. Bimodal zinc and iron oxide-based MRI/optical nanoparticle contrast agents (Zn(Fe)O) have been synthesised with a novel azeotropicpolyol method in glycol solvents (DEG and PG). The most potent NPs, as regard to their MR contrast power, have been coated with carboxymethyl pullulan, polyethylene glycol,carboxymethyl dextran (CMD) and fucoidan, the latter being a polysaccharide able to specifically bind to the vascular wall. The coated NPs were injected into rat to locate atherothrombosis by MRI. Then the histological slices of harvested diseased tissue were imaged with our homemade optical microscope. Water removal using Dean-Stark apparatus is a novel strategy for the synthesis of NPs in polyol solution with high yield and small size.The NPs show the good magnetic and optical properties at room temperature. The coated nanoparticles were injected into an atherothrombotic rat model to locate the thrombus by MRI prior to sacrifice of the animals and tissue collection for histological study by optical microscopy. The difference of MRI images between before and after injection with Fucoidan-NPs and CMD-NPs is clear. The results indicated that fucoidan-NPs are linked to the thrombus. Some type of microscopies, such as fluorescent microscopy, dark field microscopy, hyperspectral dark field microscopy and interference dark field microscopy have been developed for the detection of NPs in liquid medium and in the histological tissue. By analyzing the spectrum of every pixel and comparing to the spectrum of reference materials, hyperspectral microscopy can detect the presence of nanomaterial on exposed tissue slices, locate, identify, and characterize them. Zn(Fe)O NPs would therefore constitute a potential bimodal contrast agent for MRI and optical imaging. Although many advance optical tools have been developed, but we found it is still a challenge to identify reliably the NPs in the tissue
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Frontera, Pons Joana Maria. "Robust target detection for Hyperspectral Imaging." Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0024/document.
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L'imagerie hyperspectrale (HSI) repose sur le fait que, pour un matériau donné, la quantité de rayonnement émis varie avec la longueur d'onde. Les capteurs HSI mesurent donc le rayonnement des matériaux au sein de chaque pixel pour un très grand nombre de bandes spectrales contiguës et fournissent des images contenant des informations à la fois spatiale et spectrale. Les méthodes classiques de détection adaptative supposent généralement que le fond est gaussien à vecteur moyenne nul ou connu. Cependant, quand le vecteur moyen est inconnu, comme c'est le cas pour l'image hyperspectrale, il doit être inclus dans le processus de détection. Nous proposons dans ce travail d'étendre les méthodes classiques de détection pour lesquelles la matrice de covariance et le vecteur de moyenne sont tous deux inconnus.Cependant, la distribution statistique multivariée des pixels de l'environnement peut s'éloigner de l'hypothèse gaussienne classiquement utilisée. La classe des distributions elliptiques a été déjà popularisée pour la caractérisation de fond pour l’HSI. Bien que ces modèles non gaussiens aient déjà été exploités dans la modélisation du fond et dans la conception de détecteurs, l'estimation des paramètres (matrice de covariance, vecteur moyenne) est encore généralement effectuée en utilisant des estimateurs conventionnels gaussiens. Dans ce contexte, nous analysons de méthodes d’estimation robuste plus appropriées à ces distributions non-gaussiennes : les M-estimateurs. Ces méthodes de détection couplées à ces nouveaux estimateurs permettent d'une part, d'améliorer les performances de détection dans un environment non-gaussien mais d'autre part de garder les mêmes performances que celles des détecteurs conventionnels dans un environnement gaussien. Elles fournissent ainsi un cadre unifié pour la détection de cibles et la détection d'anomalies pour la HSIHyperspectral imaging (HSI) extends from the fact that for any given material, the amount of emitted radiation varies with wavelength. HSI sensors measure the radiance of the materials within each pixel area at a very large number of contiguous spectral bands and provide image data containing both spatial and spectral information. Classical adaptive detection schemes assume that the background is zero-mean Gaussian or with known mean vector that can be exploited. However, when the mean vector is unknown, as it is the case for hyperspectral imaging, it has to be included in the detection process. We propose in this work an extension of classical detection methods when both covariance matrix and mean vector are unknown.However, the actual multivariate distribution of the background pixels may differ from the generally used Gaussian hypothesis. The class of elliptical distributions has already been popularized for background characterization in HSI. Although these non-Gaussian models have been exploited for background modeling and detection schemes, the parameters estimation (covariance matrix, mean vector) is usually performed using classical Gaussian-based estimators. We analyze here some robust estimation procedures (M-estimators of location and scale) more suitable when non-Gaussian distributions are assumed. Jointly used with M-estimators, these new detectors allow to enhance the target detection performance in non-Gaussian environment while keeping the same performance than the classical detectors in Gaussian environment. Therefore, they provide a unified framework for target detection and anomaly detection in HSI
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Yijian, Meng. "Extreme Ultraviolet Hyperspectral Coherent Diffractive Imaging." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/31928.
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We demonstrate hyperspectral imaging using two time-delayed, coherent extreme ultraviolet (XUV) sources. The approach combines broadband XUV high-harmonic generation, holographic imaging, and Fourier transform spectroscopy. The two harmonics sources are spatially separated at generation,and overlap in the far field resulting in a double slit diffraction pattern. We record the two-dimensional intensity modulation as a function of relative time delay; the Fourier transform determines the spatially dependent spectrum. To reduce the delay jitter and improve the spectral resolution, we demonstrate a novel experimental setup that records the relative delay of the two pulses through optical interference. Moreover, we have demonstrated that this broadband approach can be extended to Fourier transform holographic imaging, which avoids extensive phase retrieval computations. Applications include imaging of biological materials near the carbon K-edge.
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Alabboud, Ied. "Human retinal oximetry using hyperspectral imaging." Thesis, Heriot-Watt University, 2009. http://hdl.handle.net/10399/2297.
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The aim of the work reported in this thesis was to investigate the possibility of measuring human retinal oxygen saturation using hyperspectral imaging. A direct non-invasive quantitative mapping of retinal oxygen saturation is enabled by hyperspectral imaging whereby the absorption spectra of oxygenated and deoxygenated haemoglobin are recorded and analysed. Implementation of spectral retinal imaging thus requires ophthalmic instrumentation capable of efficiently recording the requisite spectral data cube. For this purpose, a spectral retinal imager was developed for the first time by integrating a liquid crystal tuneable filter into the illumination system of a conventional fundus camera to enable the recording of narrow-band spectral images in time sequence from 400nm to 700nm. Postprocessing algorithms were developed to enable accurate exploitation of spectral retinal images and overcome the confounding problems associated with this technique due to the erratic eye motion and illumination variation. Several algorithms were developed to provide semi-quantitative and quantitative oxygen saturation measurements. Accurate quantitative measurements necessitated an optical model of light propagation into the retina that takes into account the absorption and scattering of light by red blood cells. To validate the oxygen saturation measurements and algorithms, a model eye was constructed and measurements were compared with gold-standard measurements obtained by a Co-Oximeter. The accuracy of the oxygen saturation measurements was (3.31%± 2.19) for oxygenated blood samples. Clinical trials from healthy and diseased subjects were analysed and oxygen saturation measurements were compared to establish a merit of certain retinal diseases. Oxygen saturation measurements were in agreement with clinician expectations in both veins (48%±9) and arteries (96%±5). We also present in this thesis the development of novel clinical instrument based on IRIS to perform retinal oximetry.
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Wood, Tobias C. "Dynamic hyperspectral and polarized endoscopic imaging." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/7062.
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The health of rich, developed nations has seen drastic improvement in the last two centuries. For it to continue improving at a similar rate new or improved diagnostic and treatment technologies are required, especially for those diseases such as cancer which are forecast to constitute the majority of disease burden in the future. Optical techniques such as microscopy have long played their part in the diagnostic process. However there are several new biophotonic modalities that aim to exploit various interactions between light and tissue to provide enhanced diagnostic information. Many of these show promise in a laboratory setting but few have progressed to a clinical setting. We have designed and constructed a flexible, multi-modal, multi-spectral laparoscopic imaging system that could be used to demonstrate several different techniques in a clinical setting. The core of this system is a dynamic hyperspectral illumination system based around a supercontinuum laser and Digital Micromirror Device that can provide specified excitation light in the visible and near infra-red ranges. This is a powerful tool for spectroscopic techniques as it is not limited to interrogating a fixed range of wavelengths and can switch between excitation bands instantaneously. The excitation spectra can be customised to match particular fluorophores or absorption features, introducing new possibilities for spectral imaging. A standard 10 mm diameter rigid endoscope was incorporated into the system to reduce cost and demonstrate compatibility with existing equipment. The polarization properties of two commercial endoscopes were characterised and found to be unsuited to current polarization imaging techniques as birefringent materials used in their construction introduce complex, spatially dependent transformations of the polarization state. Preliminary exemplar data from phantoms and ex vivo tissue was collected and the feasibility and accuracy of different analysis techniques demonstrated including multiple class classification algorithms. Finally, a novel visualisation method was implemented in order to display the complex hyperspectral data sets in a meaningful and intuitive way to the user.
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Aikio, Mauri. "Hyperspectral prism-grating-prism imaging spectrograph /." Espoo [Finland] : Technical Research Centre of Finland, 2001. http://www.vtt.fi/inf/pdf/publications/2001/P435.pdf.
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Steutel, Donovan. "Efficient Materials Mapping Using Hyperspectral Imaging Data." Thesis, University of Hawaii at Manoa, 2002. http://hdl.handle.net/10125/6962.
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Hyperspectral images contain large amounts of spectral data. An efficient material identification (EMI) process can incorporate methods which reduce the amount of spectra analyzed in a hyperspectral image and interpret the image quickly while still maximizing the quality of interpretation of the image. The purposes of this study are to implement and evaluate an EMI process, determine ways to improve the process, and to implement and test those improvements. An EMI process using spectral endmember detection, linear unmixing, and automated spectral endmember material identification by spectral feature matching is used to analyze a visible near-infrared hyperspectral image of Kaneohe Bay, Hawaiʻi, a region containing a complex mixture of natural and manmade elements. The EMI technique is successfully applied. Evaluation of the resultant interpretation of the hyperspectral image reveals shortcomings in the EMI process in endmember detection and material identification. Particularly, some detected endmembers are spectral targets useful only for mapping a small portion of image, and the library material database of the feature matching algorithm is insufficiently matched to materials in the Kaneohe Bay scene. Two improvements to the spectral endmember detection technique used in the EMI process are proposed: target detection and masking and more than one evaluation of image pixels as potential spectral endmembers. These proposed improvements are incorporated into a subsequent analysis of the Kaneohe Bay scene, resulting in an improved material analysis of the scene. The improvement is primarily due to the incorporation of target detection only. The EMI process is also applied to a multispectral image of the Aristarchus Plateau on the Moon. Target masking is incorporated into endmember detection, and a different material identification algorithm, one based on radiative transfer theory, is used. Highly detailed maps of lunar mineralogy and rock types are produced which are consistent with previous spectral analyses of the Aristarchus region. These maps add to previous findings in detail and specificity of location and quantity of mineral and rock distributions on the Aristarchus Plateau.xvi, 117 leaves
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Tendenes, Nils Ove. "LED light source for hyperspectral fluorescence imaging." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-19193.
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This report deals with the possibility of creating a LED light source, to be used withhyperspectral fluorescence imaging. There are commercially available light sources thatcould be used, but they are expensive, they do not necessarily emit the right wavelength, the uniformity of the field is questionable and they are difficult to modify.First a batch of Light emitting diodes were acquired, these were subjected to a seriesof tests to classify their limitations and determine which diodes were to be included in the final light source. A spectrometer was used to determine the emitted wavelength of each diode and which scenarios could change the wavelength of the emitted light. Aphotodiode was used to acquire the viewing angle of the LEDs and their relative radiantpower. Images gathered by a hyperspectral camera were used to determine the relevancyof noise produced by the current source. When the light emitting diodes were chosen,the photodiode was used to make an image of the light field. The final light source wasmounted on the hyperspectral camera to gather fluorescent images.The final tests revealed a fully functional light source with potential to be used on aregular basis, but the current source was too cumbersome and the field was not optimal.These are issues that can be dealt with and this light source can in the future provide a cheap and easily modifiable light source alternative.
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Myers, Travis R. (Travis Ray). "Active hyperspectral imaging of chemicals on surfaces." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/112385.
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Thesis: S.M. in Materials Science, Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2014.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 90-92).
Active hyperspectral imaging (HSI) is a promising technique for the detection of chemicals at standoff distances. In active HSI, a target is illuminated by a laser source at many different wavelengths and a camera obtains an image of the illuminated scene at each wavelength. In this research, the feasibility of hyperspectral imaging for the detection of particles on surfaces was demonstrated using potassium chlorate particles on car panels at distances of 5 m, 10 m, and 20 m. Using the Adaptive Cosine Estimation (ACE) algorithm which compares the observed reflectance spectra to a reference spectrum, potassium chlorate fingerprints are easily visible at many different sample angles. However, in general, there is a large amount of variation in the shape and magnitude of spectra in a hyperspectral image that depend on factors such as particle size, viewing geometry, and surface reflectivity. Thus, Mie Theory calculations are performed on simulated materials and combined with information from sources such as Hapke [4], [20] to give qualitative insight into the expected shape and magnitude of reflectance spectra from sparse particles on a surface. The shape of the spectra depends on whether the particles are strongly absorbing or weakly absorbing. Strongly absorbing particles tend to have reflectance maxima near the resonant frequency, whereas weakly absorbing particles tend to have reflectance minima. For highly reflective substrates, the reflectance decreases sharply as the sample angle increases and becomes dominated by backward scattering from the particle which has a flatter spectrum around the Christiansen frequency, the frequency at which the refractive index of the particle is closest to one. The double interaction model, which uses Mie Theory to calculate the contributions to the reflectance along two different light paths, is used to accurately account for how the shape and magnitude of the reflectance spectra of sodium chlorate particles on gold and silica surfaces changes as a function of sample angle and laser angle. A method for approximating the mean particle size based on the location of the peak near the Christiansen frequency is derived. This method, when applied to the sodium chlorate sample, yields a result for the mean particle diameter that is approximately half of the value determined using a microscope. The Hapke Isotropic Multiple Scattering Approximation (IMSA), combined with Mie Theory, is used to give qualitative insight into the expected shape and magnitude of reflectance spectra from bulk powders. Compared with the reflectance spectra from sparse particles, the spectra from bulk powders are much simpler and less dependent on the viewing geometry. The Hapke IMSA model is able to accurately account for the observed changes in the reflectance from bulk sodium chlorate powder at multiple sample angles and laser angles. A final scenario of interest is thin films on rough or porous surfaces. Using a model that takes into account diffusely reflected and specularly reflected light, the observed reflectance spectra from diethyl phthalate (DEP) on a brick is fitted to a high degree of accuracy. This suggests a promising method for using hyperspectral imaging to determine the thickness of liquids on porous surfaces. Finally, the issue of speckle in hyperspectral imaging was examined using simulations based on Fourier optics and information from sources such as Goodman [6], [17]. Speckle is a limiting factor in hyperspectral imaging because it is noise that scales with the signal, and thus cannot be eliminated by increasing the signal strength. Equations from various sources are presented that describe the reduction in speckle contrast for spatial, spectral, polarization, temporal, and angular averaging. Original equations for the reduction in contrast for spectral and angular averaging are derived.
by Travis R. Myers.
S.M. in Materials Science
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Muench, Joseph. "Application of hyperspectral imaging in pharmaceutical analysis." Thesis, University of Strathclyde, 2014. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=23626.
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This thesis describes the application of hyperspectral imaging (HSI) as a novel technique for the analysis of spectral data derived from image analysis of tablet breakdown during dissolution. Whilst defining the rate of release is the common output from traditional dissolution experiments, the intention of the research presented in this thesis was also to describe the physical changes occurring within the disintegrated mass during the dissolution process. The initial stages of the investigation focused on determining which wavelength ranges were most discriminatory in separating out similar polymer samples; the two wavelength ranges investigated were the visible (400-860 nm) and the near infrared (835-1650 nm). As the polymer samples were similar, comparison of the spectra for distinguishing features was unsuccessful and principal component analysis was used to separate the spectral signals. The near infrared (nIR) was shown to be the most effective wavelength range at separating s ignals due to an increased number of peaks for comparative analysis. The examination of the dissolution of paracetamol tablets produced by several different manufacturers was described in this thesis. In particular, the rate of expansion of tablet material and the identification of caffeine containing regions during tablet dissolution. The initial results tracked the expansion of the tablet within the flow cell and determined the cause of the signal attenuation affecting the results. The final experiments combined the spatial and time resolutions of the previous experiments. This system used a minimised path length to reduce signal attenuation and give spectra with enhanced spectral features. This system was able to show caffeine rich regions in the tablet during the dissolution in a caffeinated paracetamol brand, the rate of caffeine loss also being calculated. These results were then corroborated using conventional analytical techniques to establish whether HSI had robustly measured the release of a known compound during dissolution.
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Ekpenyong, Nsikak Edet. "Hyperspectral imaging : calibration and applications with natural scenes." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/hyperspectral-imaging-calibration-and-applications-with-natural-scenes(305307e2-0a47-4273-b5b4-1a86de535f1e).html.
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Hyperspectral imaging is a technique which combines spectral and spatial imaging methods. The technology is used in remote sensing, medicine, agriculture and forensics just to mention a few. Non-remote systems are developed by using sensor designs different from push-broom and whisk-broom methods, commonly found in remote sensing hyperspectral imaging systems. Images are commonly acquired by mounting various electronically tunable filters in front of monochromatic cameras and capturing a range of wavelengths to produce a spectral image cube. Illumination plays a major role during imaging, as both the camera and electronically tunable filter may suffer low transmission at the ends of the visible spectrum, resulting in a low signal to noise ratio. The work described in this thesis attempts to address two key objectives. The first was to identify the main sources of errors in a common design of focal-plane hyperspectral imaging system and devise ways of compensating for these errors. Calibration and characterization of a focal-plane hyperspectral imaging system included system noise characterization, stray-light compensation, flat field correction, image registration, input-output function characterization and calibration verification. The other was to apply imaging techniques to hyperspectral images. This included scene recognition using ratio indexing and spectral gradients. This comes from the underlying idea that due to the large number of bands contained in hyperspectral images, more information is available so better recognition results compared to RGB images. A novel approach for obtaining ratios for ratio indexing is proposed in this thesis. The imaging of archived materials from University of Manchester's John Rylands Library was also done. The aim was to produce high resolution hyperspectral images that will help in identifying accurate matches for colours used in document restoration at the Library.
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Gu, Yanyang. "Melanoma Detection Based on Color and Hyperspectral Imaging." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386570.
Full textAbstract:
Melanoma is the most fatal form of all skin cancer types, being diagnosed mostly among lightly pigmented skin. An early screening of melanoma can greatly contribute to successful treatment, hence reliable early detection systems are highly demanded and key technologies need to be developed.
The existing automated melanoma detection algorithms are dominantly based on color images. Early methods adopted machine vision algorithms which require hand-crafted features to be designed. With the development of machine learning models and the
access to the large skin image datasets, deep learning has been introduced for melanoma detection so effective feature can be automatically learned. However, the research on this topic is at its early stage.
While machine learning models can be created for skin cancer detection, this task can also be boosted by introducing new imaging technology beyond the traditional color imaging process. To this end, hyperspectral images show its advantage because of their multiple spectral bands, thereby providing extra reflectance information that is related to the intrinsic properties of skins and its composition. The challenges on this technology are that there is no open dataset to support the research and how to effectively use the spectral and spatial information in the hyperspectral images for melanoma detection
remains unsolved. To address the above issues, in this thesis, we introduce three methods for melanoma detection.
The first method is based on machine vision. This method follows the common image classifi cation pipeline, i.e. pre-processing, segmentation, feature extraction, and classifi cation. The novelty of this method is that before classifi cation, we introduce a dimensionality reduction method to the extracted features as a post-processing step. This post-processing procedure is based on Mahalanobis distance learning and constrained graph regularized nonnegative matrix factorization. The proposed method allows supervised learning for feature dimensionality reduction by incorporating both global geometry
and local manifold, so as to enhance the discriminability of the classifi cation performance. The proposed method is evaluated on PH2 Dermoscopy Image Dataset and Edinburgh Dermofi t Image Library, with comparison against four alternative classifi cation methods. The experimental results demonstrate that the best performance is achieved with the proposed method compared with another NMF baseline method and
direct classifi cation without post-processing.
The second melanoma detection method is deep learning-based. Deep learning is a datadriven
technique that does not require hand-crafted feature design, thereby improving
the generalization capacity of the model. However, a well-trained deep learning model
from one dataset often cannot be generalized to other datasets, even when all datasets
have the same categories. This is mainly because of the domain shift between datasets
of different cohorts in the data capture process. On this regards, we exploit two methods
to relieve this issue by evaluating on two different skin disease datasets, MoleMap and
HAM10000. The fi rst option is parameter-based transfer learning. We use a progressive
transfer learning scheme to share transferable knowledge between multiple datasets, i.e.
transferring knowledge from a task-different source dataset (ImageNet) to a category-same
but dataset-different intermediate dataset (MoleMap) and at last to the target
dataset (HAM10000). For the second option, we use cycle-consistent generative adversarial
networks to translate the images from the source domain into the target domain
for pixel-wise image adaptation. The synthesized image data are integrated with the
training samples in the original target domain during the training stage, therefore forming
the methods of dataset adaptation and modality domain adaptation. The results of
progressive transfer learning show that it achieves better performance and generalization
capacity than 1-step transfer learning model and model training from scratch. Furthermore,
both dataset adaptation and modality domain adaptation show improvements of
the model generalization capacity, melanoma detection, skin cancer detection, and skin
disease classi fication.
The third method is by means of hyperspectral imaging. Besides the spatial information,
hyperspectral imaging provides fine resolution in spectral wavelength. With the
abundant spectral-spatial information, hyperspectral imaging can facilitate melanoma
detection. In this research, we introduce a hyperspectral dermoscopic dataset and describe
a detailed description of the hardware and software of the data collection system
developed in the Spectral Imaging Lab of Griffth University. As far as we know, this is
the fi rst open hyperspectral dermoscopic benchmark dataset. Based on this dataset, we
provide the baselines using machine learning methods, which include sparse coding, support
vector machine, and deep learning. We show the performance of spatial features,
spectral features and joint spectral-spatial features on this dataset. The experiments
show that the classifi cation performance is improved with extra spectral features.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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Nanyam, Yasasvy. "Hyperspectral Imaging for Nondestructive Measurement of Food Quality." OpenSIUC, 2010. https://opensiuc.lib.siu.edu/theses/334.
Full textAbstract:
This thesis focuses on developing a nondestructive strategy for measuring the quality of food using hyperspectral imaging. The specific focus is to develop a classification methodology for detecting bruised/unbruised areas in hyperspectral images of fruits such as strawberries through the classification of pixels containing the edible portion of the fruit. A multiband segmentation algorithm is formulated to generate a mask for extracting the edible pixels from each band in a hypercube. A key feature of the segmentation algorithm is that it makes no prior assumptions for selecting the bands involved in the segmentation. Consequently, different bands may be selected for different hypercubes to accommodate the intra-hypercube variations. Gaussian univariate classifiers are implemented to classify the bruised-unbruised pixels in each band and it is shown that many band classifiers yield 100% classification accuracies. Furthermore, it is shown that the bands that contain the most useful discriminatory information for classifying bruised-unbruised pixels can be identified from the classification results. The strategy developed in this study will facilitate the design of fruit sorting systems using NIR cameras with selected bands.
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Mertiri, Alket. "Mid-infrared photothermal hyperspectral imaging of biomolecular systems." Thesis, Boston University, 2014. https://hdl.handle.net/2144/12952.
Full textAbstract:
Thesis (Ph.D.)--Boston UniversityThe development of novel techniques in spectroscopy and microscopy that are label-free, contactless and accessible is useful among many scientific disciplines, ranging from Materials Science to Biomedical Engineering. Hyperspectral photothermal imaging using vibrational spectroscopy promises to be a new tool in the arsenal for analysis and characterization of materials. This technique can be used for understanding structural composition of a material that is advantageous to the materials scientist. A combination of microscopy and spectroscopy is also beneficial to the biologist or pathologist that analyzes a complex sample with rich morphology. Photothermal hyperspectral microscopy is a label-free nondestructive method that utilizes specific vibrational bands of a molecule giving spectral information to an image. The method is based on changes in the thermal state, and the associated change in the refractive index of the sample as it is irradiated with mid-infrared light. Photothermal microscopy has rapidly emerged as one of the most sensitive label-free optical spectroscopic methods, rivaling current well-established methods based on fluorescence. The method has been used to image single non-fluorescent molecules in room temperature and to directly characterize biological features such as mitochondria and red blood cells. Despite great breakthroughs in the visible regime, the method has not been explored in the mid-infrared regime where most of the biological molecules have characteristic vibrational modes that constitute an intrinsic molecular "fingerprint" . This thesis presents the development of a new technique to measure the linear and nonlinear mid-infrared photothermal response induced by tunable high power lasers such as Quantum Cascade Lasers (QCLs). Photothermal response can be measured in pump-probe heterodyne detection, using short wavelength visible lasers and compact fiber lasers as a probe. This allows for direct detection of the fingerprint mid-infrared vibrational modes through ultrasensitive photodetectors. Integrated into a mid-infrared microscope, the system facilitates the acquisition of spectra and images on condensed phase samples. Photothermal heterodyne mid-infrared hyperspectral vibrational technique is used to image biological samples such as bird brain and other biomolecules First photothermal images on specially designed plasmonic metamaterials, designed to either enhance or suppress a selected mid-infrared vibrational normal mode, are demonstrated. Plasmonic metamaterials can be engineered using electron beam lithography for functional studies on biomolecules enhancing selected vibrational infrared resonances. This study takes advantage of the strong interaction between light and matter and investigates properties of the material that are difficult to detect through conventional spectroscopic methods. The new technique has the ability to advance studies in many fields, as it is applicable to different types of materials, non-destructive, accessible and inexpensive.
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Dowler, Shaun Wallace. "Applications of hyperspectral imaging techniques to forensic image analysis." Thesis, University of Auckland, 2010. http://hdl.handle.net/2292/9604.
Full textAbstract:
Hyperspectral imaging is a form of imaging spectroscopy developed for remote sensing. Hyperspectral algorithms have many useful properties: particularly robustness to scene conditions and the versatility to analyse a wide variety of scene compositions. Hyperspectral techniques are, however, computationally expensive. Imaging spectroscopy has been applied to the analysis of forensic crime scenes in the recent past with some success. The relative simplicity of the techniques used in these studies, however, has created an opportunity to apply hyperspectral techniques to forensic scenes. This work focused on the development of analysis techniques for camera systems suitable for imaging forensic scenes in the field. Hyperspectral unmixing allows for a scene to be decomposed into a list of material signatures and maps of the abundances of those materials. Winter’s N-FINDR was selected as a suitable unmixing technique for examination due to its popularity, performance and well-understood operation. Analyses of the operation, complexity and performance on simulated and real remote sensing scenes of N-FINDR were conducted to establish a baseline against the body of remote sensing literature. N-FINDR was shown to be an effective, albeit computationally costly, algorithm for analysing hyperspectral data. Two complementary means for reducing the complexity of the N-FINDR algorithm were considered. The algorithm was restructured and the use of an LDU decomposition allowed for redundancies in the computations to be removed. Secondly, a means for reducing the search space of the algorithm was examined and shown to have a favourable complexity-accuracy trade-off. These modifications allow for N-FINDR to form the basis of a hyperspectral still camera system. A new algorithm, Abundance Guided Endmember Selection (AGES), was developed with the property that iterations have low complexity and produce intermediate material maps. A modified version of AGES was used to develop a framework for a video camera system that made use of between-frame redundancy. Both N-FINDR and AGES were compared to more traditional techniques from forensic literature in their performance on blood shoemarks and treated fingermarks. On these scenes, NFINDR and AGES were shown to equal or outperform traditional techniques. The work constitutes major progress towards a system capable of field deployment.
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Green, Faith H. "Hyperspectral X-ray imaging for scatter removal in mammography." Thesis, University of Surrey, 2016. http://epubs.surrey.ac.uk/809905/.
Full textAbstract:
The purpose of this study is to investigate the use of hyperspectral X-ray imaging i.e, an imaging modality where spectral information on detected X-rays is available, for the removal of scatter in mammography. Features that suggest the presence of cancer are often low in contrast or small in size and therefore a good image quality is required in order to locate them. Current mammography systems use an anti-scatter grid to remove scatter from the image. However, this device also absorbs a proportion of the primary beam and therefore a rise in the dose is required in order to compensate for the loss in statistics. Two alternative methods of scatter removal were investigated in this study. Compton scattered X-rays lose energy in the scattering process and therefore appear at a lower energy in the detected spectrum. Imaging using a monochromatic X-ray beam and a spectroscopic detector means that these lower energy scattered X-rays can be removed from the image through spectral windowing. An alternative method of removing scatter is to simulate the scatter using Monte Carlo modelling. Once a scatter spectrum has been obtained using an input spectrum similar to the experimental spectrum it can be subtracted from the detected spectrum, creating a scatter-free image. This work presents imaging work carried out with a pixellated spectroscopic CdTe detector. A first approach involved the use of a mosaic crystal monochromator producing a quasimonochromatic spectrum from which the scattered component can be removed. The second approach involved the subtraction of the scattered spectrum, as obtained from Monte-Carlo modelling, from a full polychromatic spectrum. Both approaches were tested on a customdesigned low contrast test object. Results showed that in the monochromatic approach scatter removal gave a 40% increase in contrast. It was also found that removing scatter using a simulated scatter spectrum and a polychromatic beam produced a contrast improvement of around 15% when compared to full spectrum imaging.
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Lankapalli, Ravikanth. "Determination of physical contaminants in wheat using hyperspectral imaging." Biosystems Engineering, 2015. http://hdl.handle.net/1993/30902.
Full textAbstract:
Cereal grains are an important part of human diet; hence, there is a need to maintain high quality and these grains must be free of physical and biological contaminants. A procedure was developed to differentiate physical contaminants from wheat using NIR (1000-1600 nm) hyperspectral imaging. Three experiments were conducted to select the best combinations of spectral pre-processing technique and statistical classifier to classify physical contaminants: seven foreign material types (barley, canola, maize, flaxseed, oats, rye, and soybean); six dockage types (broken wheat kernels, buckwheat, chaff, wheat spikelets, stones, and wild oats); and two animal excreta types (deer and rabbit droppings) from Canada Western Red Spring (CWRS) wheat. These spectra were processed using five spectral pre-processing techniques (first derivative, second derivative, Savitzky-Golay (SG) smoothing and differentiation, multiplicative scatter correction (MSC), and standard normal variate (SNV)). The raw and pre-processed data were classified using Support Vector Machines (SVM), Naïve Bayes (NB), and k-nearest neighbors (k-NN) classifiers. In each experiment, two-way and multi-way classifications were conducted.
Among all the contaminant types, stones, chaff, deer droppings and rabbit droppings were classified with 100% accuracy using the raw reflectance spectra and different statistical classifiers. The SNV technique with k-NN classifier gave the highest accuracy for the classification of foreign material types from wheat (98.3±0.2%) and dockage types from wheat (98.9±0.2%). The MSC and SNV techniques with SVM or k-NN classifier gave perfect classification (100.0±0.0%) for the classification of animal excreta types from wheat. Hence, the SNV technique with k-NN classifier was selected as the best model.
Two separate model performance evaluation experiments were conducted to identify and quantify (by number) the amount of contaminant type present in wheat. The overall identification accuracy of the first degree of contamination (one contaminant type with wheat) and the highest degree of contamination (all the contaminant type with wheat) was 97.6±1.6% and 92.5±6.5%, for foreign material types; 98.0±1.8% and 94.3±6.2%r for dockage types; and 100.0±0.0% and 100.0±0.0%, respectively for animal excreta types. The canola, stones, deer, and rabbit droppings were perfectly quantified (100.0±0.0%) at all the levels of contaminations.February 2016
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KHODOR, MAHDI. "Landmine detection using hyper-spectral imaging." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2922912.
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Nandi, David Anil. "The use of hyperspectral imaging for remote sensing, and the development of a novel hyperspectral imager." Thesis, Durham University, 2014. http://etheses.dur.ac.uk/11824/.
Full textAbstract:
This thesis determines the potential uses of a novel technology in hyperspectral remote sensing, by testing the capabilities of a prototype imaging spectrometer that was built using microslice technology. These capabilities are compared to those of current hyperspectral remote sensing instruments in the context of the requirements for various remote sensing applications. Due to the wide variety of potential applications for hyperspectral imaging, any unique capability of a new instrument is likely to improve a current application, or even develop a new one. The use of microslice technology allows a 2-dimensional eld of view (FoV) to be imaged simultane ously with a wide spectral range. Modelling of the remote sensing performance of the spectrometer shows that this enables it to achieve a signal to noise ratio (SNR) an order of magnitude higher than conventional hyperspectral instruments. The prototype microslice spectrometer images in the 475-650 nm wavelength range at 7 nm spectral resolution. It also images an instantaneous eld of view (IFoV) of 260 x 52 mrad, at a spatial resolution of 2.6 mrad. Classication techniques are used on ground based laboratory and eld test data from the instrument to demonstrate that it can accurately identify some mineral, vegetation, and water pollutant samples. Various trade-os can theoretically be performed on the prototype specications to develop an instru ment with particular capabilities for a specic application. This novel design means that a greater detector area is required than for conventional designs; but the 2-dimentsional FoV gives greater trade-o exibility, in particular allowing the SNR to enter into the trade-o equation. This unique capability was found to lend itself to two applications in particular: detecting water pollutants in rivers, and detecting hydrocarbons contamination of ecosystems.
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Wong, Gerald. "Snapshot hyperspectral imaging : near-infrared image replicating imaging spectrometer and achromatisation of Wollaston prisms." Thesis, Heriot-Watt University, 2012. http://hdl.handle.net/10399/2615.
Full textAbstract:
Conventional hyperspectral imaging (HSI) techniques are time-sequential and rely on temporal scanning to capture hyperspectral images. This temporal constraint can limit the application of HSI to static scenes and platforms, where transient and dynamic events are not expected during data capture. The Near-Infrared Image Replicating Imaging Spectrometer (N-IRIS) sensor described in this thesis enables snapshot HSI in the short-wave infrared (SWIR), without the requirement for scanning and operates without rejection in polarised light. It operates in eight wavebands from 1.1μm to 1.7μm with a 2.0° diagonal field-of-view. N-IRIS produces spectral images directly, without the need for prior topographic or image reconstruction. Additional benefits include compactness, robustness, static operation, lower processing overheads, higher signal-to-noise ratio and higher optical throughput with respect to other HSI snapshot sensors generally. This thesis covers the IRIS design process from theoretical concepts to quantitative modelling, culminating in the N-IRIS prototype designed for SWIR imaging. This effort formed the logical step in advancing from peer efforts, which focussed upon the visible wavelengths. After acceptance testing to verify optical parameters, empirical laboratory trials were carried out. This testing focussed on discriminating between common materials within a controlled environment as proof-of-concept. Significance tests were used to provide an initial test of N-IRIS capability in distinguishing materials with respect to using a conventional SWIR broadband sensor. Motivated by the design and assembly of a cost-effective visible IRIS, an innovative solution was developed for the problem of chromatic variation in the splitting angle (CVSA) of Wollaston prisms. CVSA introduces spectral blurring of images. Analytical theory is presented and is illustrated with an example N-IRIS application where a sixfold reduction in dispersion is achieved for wavelengths in the region 400nm to 1.7μm, although the principle is applicable from ultraviolet to thermal-IR wavelengths. Experimental proof of concept is demonstrated and the spectral smearing of an achromatised N-IRIS is shown to be reduced by an order of magnitude. These achromatised prisms can provide benefits to areas beyond hyperspectral imaging, such as microscopy, laser pulse control and spectrometry.
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Atas, Musa. "Hyperspectral Imaging And Machine Learning Of Texture Foods For Classification." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613883/index.pdf.
Full textAbstract:
In this thesis the main objective is to design a machine vision system that classifies aflatoxin contaminated chili peppers from uncontaminated ones in a rapid and non-destructive manner via hyperspectral imaging and machine learning techniques. Hyperspectral image series of chili pepper samples collected from different regions of Turkey have been acquired under halogen and UV illuminations. A novel feature set based on quantized absolute difference of consecutive spectral band features is proposed. Spectral band energies along with absolute difference energies of the consecutive spectral bands are utilized as features and compared with other feature extraction methods such as Teager energy operator and 2D wavelet Linear Discriminant Bases (2D-LDB). For feature selection, Fisher discrimination power, information theoretic Minimum Redundancy Maximum Relevance (mRMR) method and proposed Multi Layer Perceptron (MLP) based feature selection schemes are utilized.Finally, Linear Discriminant Classifier (LDC), Support Vector Machines (SVM) and MLP are used as classifiers. It is observed that MLP outperforms other learning models in terms of predictor performance. We verified the performance and robustness of our proposed methods on different real world datasets. It is suggested that to achieve high classification accuracy and predictor robustness, a machine vision system with halogen excitation and quantized absolute difference of consecutive spectral band features should be utilized.
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Gosselin, Ryan. "ON-LINE QUALITY CONTROL IN POLYMER PROCESSING USING HYPERSPECTRAL IMAGING." Thesis, Université Laval, 2010. http://www.theses.ulaval.ca/2010/26629/26629.pdf.
Full textAbstract:
L’industrie du plastique se tourne de plus en plus vers les matériaux composites afin d’économiser de la matière et/ou d’utiliser des matières premières à moindres coûts, tout en conservant de bonnes propriétés. L’impressionnante adaptabilité des matériaux composites provient du fait que le manufacturier peut modifier le choix des matériaux utilisés, la proportion selon laquelle ils sont mélangés, ainsi que la méthode de mise en œuvre utilisée.
La principale difficulté associée au développement de ces matériaux est l’hétérogénéité de composition ou de structure, qui entraîne généralement des défaillances mécaniques. La qualité des prototypes est normalement mesurée en laboratoire, à partir de tests destructifs et de méthodes nécessitant la préparation des échantillons. La mesure en-ligne de la qualité permettrait une rétroaction quasi-immédiate sur les conditions d’opération des équipements, en plus d’être directement utilisable pour le contrôle de la qualité dans une situation de production industrielle.
L’objectif de la recherche proposée consiste à développer un outil de contrôle de qualité pour la qualité des matériaux plastiques de tout genre. Quelques sondes de type proche infrarouge ou ultrasons existent présentement pour la mesure de la composition en-ligne, mais celles-ci ne fournissent qu’une valeur ponctuelle à chaque acquisition. Ce type de méthode est donc mal adapté pour identifier la distribution des caractéristiques de surface de la pièce (i.e. homogénéité, orientation, dispersion).
Afin d’atteindre cet objectif, un système d’imagerie hyperspectrale est proposé. À l’aide de cet appareil, il est possible de balayer la surface de la pièce et d’obtenir une image hyperspectrale, c’est-à-dire une image formée de l’intensité lumineuse à des centaines de longueurs d’onde et ce, pour chaque pixel de l’image. L’application de méthodes chimiométriques permettent ensuite d’extraire les caractéristiques spatiales et spectrales de l’échantillon présentes dans ces images. Finalement, les méthodes de régression multivariée permettent d’établir un modèle liant les caractéristiques identifiées aux propriétés de la pièce. La construction d’un modèle mathématique forme donc l’outil d’analyse en-ligne de la qualité des pièces qui peut également prédire et optimiser les conditions de fabrication.The use of plastic composite materials has been increasing in recent years in order to reduce the amount of material used and/or use more economic materials, all of which without compromising the properties. The impressive adaptability of these composite materials comes from the fact that the manufacturer can choose the raw materials, the proportion in which they are blended as well as the processing conditions. However, these materials tend to suffer from heterogeneous compositions and structures, which lead to mechanical weaknesses. Product quality is generally measured in the laboratory, using destructive tests often requiring extensive sample preparation. On-line quality control would allow near-immediate feedback on the operating conditions and may be transferrable to an industrial production context. The proposed research consists of developing an on-line quality control tool adaptable to plastic materials of all types. A number of infrared and ultrasound probes presently exist for on-line composition estimation, but only provide single-point values at each acquisition. These methods are therefore less adapted for identifying the spatial distribution of a sample’s surface characteristics (e.g. homogeneity, orientation, dispersion). In order to achieve this objective, a hyperspectral imaging system is proposed. Using this tool, it is possible to scan the surface of a sample and obtain a hyperspectral image, that is to say an image in which each pixel captures the light intensity at hundreds of wavelengths. Chemometrics methods can then be applied to this image in order to extract the relevant spatial and spectral features. Finally, multivariate regression methods are used to build a model between these features and the properties of the sample. This mathematical model forms the backbone of an on-line quality assessment tool used to predict and optimize the operating conditions under which the samples are processed.
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Metcalf, Jeremy P. "Detecting and characterizing nighttime lighting using multispectral and hyperspectral imaging." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/27869.
Full textAbstract:
Multispectral imagery (MSI) of Las Vegas, Nevada, were investigated to determine their potential for accurately mapping nocturnal lighting using a reference spectral library of lighting types. Nocturnal lighting classifications of International Space Station (ISS) astronaut color photography and 8-, 6-, and 4-band MSI generated by modeling high spectral resolution hyperspectral imagery (HSI) data to lower spectral resolution were compared to lighting identification accomplished using the full resolution HSI spectral signatures. The results indicate that ISS imagery does not have the spectral resolution necessary to accurately distinguish between the emission features of outdoor lighting. The modeled multispectral band configurations demonstrated somewhat improved separation of certain lighting types by their spectral signatures, however, with only 8, 6, or 4 spectral bands, accurate discrimination of lighting types still remains a daunting task. While the different colors associated with outdoor lighting can be visually delineated in MSI data, the limited spectral information does not allow for accurate lighting type classification because of the inability to identify specific sharp emission features. Mapping nocturnal lighting using MSI data does have some utility, and certainly would provide better spatial coverage, however, HSI remains the most accurate method to differentiate the emission features associated with urban outdoor lighting.
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Han, Zhimin. "Hyperspectral endoscopy imaging: system development, clinical evaluation, and further application." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/55026.
Full textAbstract:
Hyperspectral (HS) imaging combines spectral measurement of a pixel with 2D imaging technology. It is capable to provide a series of images containing both spectral and spatial information, and has been widely used in medical domain. However, most researches on medical HS imaging are regarding ex-vivo biopsy or skin and oral mucosa. The study on HS imaging regarding in-vivo disease lags far behind.
In this thesis, we developed a novel flexible HS endoscope system. It is capable to obtain a series of HS images in vivo in a non-contact way among the wavelength range of 405 – 665 nm. After a lot of time-consuming modifying and debugging work, this new system has high stability and convenience to be applied in clinic now. We evaluated this system in clinic. First, we got ethics approval for clinical trials. Then, we obtained HS images regarding gastrointestinal (GI) diseases inside patients using this system. As far as we know, this type of in-vivo image data has not been reported in previous literatures. Thus using these HS images, we built a database for GI mucosa. Next, we analyzed some typical HS images tentatively. The method of Recursive Divergence is implemented to extract valuable and diagnostic information from these HS images. The results prove the effect and applicability of this new HS endoscope system, which has shown the great potential to be used as a platform and guidance for further medical studies. To further apply the analysis results in clinic, we propose a novel Adaptive Narrow-Band Imaging (ANBI) method based on band selection of HS images of a specific type of disease. It is expected that the new technique has higher accuracy, sensitivity, and specificity compared to conventional Narrow-Band Imaging (NBI) technique. In this thesis, we also discuss the future direction of the system improvement. Especially, to improve light intensity and signal-noise-ratio of HS images in wide-field view, we propose a new imaging method using broad- and overlapped-band filters. Although this method only performs greatly on the foundation of accurate image registration, we hope to apply it in our system in the future.
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Sahu, Amrita. "Hyperspectral Imaging to Discern Malignant and Benign Canine Mammary Tumors." Master's thesis, Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/224675.
Full textAbstract:
Electrical EngineeringM.S.E.E.
Hyperspectral imaging is an emerging technology in the field of biomedical engineering which may be used as a non-invasive modality to characterize tumors. In this thesis, a hyperspectral imaging system was used to characterize canine mammary tumors of unknown histopathology (pre-surgery) and correlate the results with the post-surgical histopathology results. The system consisted of a charge coupled device (CCD) camera, a liquid crystal tunable filter in the near infrared range (650-1100 nm), and a controller. Spectral signatures of malignant and benign canine mammary tumors were extracted and analyzed. The reflectance intensities of malignant tumor spectra were generally lower than benign tumor spectra over the wavelength range 650-1100nm. Previous studies have shown that cancerous tissues have a higher hemoglobin and water content, and lower lipid concentration with respect to benign tissues. The decreased reflectance intensity observed for malignant tumors is likely due to the increased microvasculature and, therefore, higher blood content of malignant tissue relative to benign tissue. Second derivative method was applied to the reflectance spectra. Peaks at 700, 840, 900 and 970 nm were observed in the second derivative reflectance spectra. These peaks were attributed to deoxy-hemoglobin, oxy-hemoglobin, lipid and water respectively. A Tissue Optical Index (TOI) was developed that enhances contrast between malignant and benign canine tumors. This index is based on the ratio of the reflectance intensity values corresponding to the wavelengths associated with the four chromophores. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were also applied on the canine spectral dataset and the method was cross-validated. Preliminary results from 22 canine mammary tumors showed that the sensitivity and specificity of the PCA-LDA is method is 86% and 86% respectively. The sensitivity and specificity of the TOI model is 86% and 95% respectively. These results show promise in the non-invasive optical diagnosis of canine mammary cancer.
Temple University--Theses
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Poon, Phillip K., Esteban Vera, and Michael E. Gehm. "Computational hyperspectral unmixing using the AFSSI-C." SPIE-INT SOC OPTICAL ENGINEERING, 2016. http://hdl.handle.net/10150/621544.
Full textAbstract:
We have previously introduced a high throughput multiplexing computational spectral imaging device. The device measures scalar projections of pseudo-arbitrary spectral filters at each spatial pixel. This paper discusses simulation and initial experimental progress in performing computational spectral unmixing by taking advantage of the natural sparsity commonly found in the fractional abundances. The simulation results show a lower unmixing error compared to traditional spectral imaging devices. Initial experimental results demonstrate the ability to directly perform spectral unmixing with less error than multiplexing alone.
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Aumiller, Riley. "Longwave Infrared Snapshot Imaging Spectropolarimeter." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/301708.
Full textAbstract:
The goal of this dissertation research is to develop and demonstrate a functioning snapshot imaging spectropolarimeter for the long wavelength infrared region of the electromagnetic spectrum (wavelengths from 8-12 microns). Such an optical system will be able to simultaneously measure both the spectral and polarimetric signatures of all the spatial locations/targets in a scene with just a single integration period of a camera. This will be accomplished by combining the use of computed tomographic imaging spectrometry (CTIS) and channeled spectropolarimetry. The proposed system will be the first instrument of this type specifically designed to operate in the long wavelength infrared region, as well as being the first demonstration of such a system using an uncooled infrared focal plane array. In addition to the design and construction of the proof-of-concept snapshot imaging spectropolarimeter LWIR system, the dissertation research will also focus on a variety of methods on improving CTIS system performance. These enhancements will include some newly proposed methods of system design, calibration, and reconstruction aimed at improving the speed of reconstructions allowing for the first demonstration of a CTIS system capable of computing reconstructions in 'real time.'
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Dunlop, Matthew, and Phillip Poon. "Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C)." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579667.
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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NVThe AFSSI-C is a spectral imager that generates spectral classification directly, in fewer measurements than are required by traditional systems that measure the spectral datacube (which is later interpreted to make material classification). By utilizing adaptive features to constantly update conditional probabilities for the different hypotheses, the AFSSI-C avoids the overhead of directly measuring every element in the spectral datacube. The system architecture, feature design methodology, simulation results, and preliminary experimental results are given.
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Chen, T. "Hyperspectral imaging for the remote sensing of blood oxygenation and emotions." Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/7502.
Full textAbstract:
This PhD project is a basic research and it concerns with how human’s physiological features, such as tissue oxygen saturation (StO2), can be captured from a stand-off distance and then to understand how this remotely acquired physiological feature can be deployed for biomedical and other applications. This work utilises Hyperspectral Imaging (HSI) within the diffuse optical scattering framework, to assess the StO2 in a contactless remote sensing manner. The assessment involves a detailed investigation about the wavelength dependence of diffuse optical scattering from the skin as well as body tissues, under various forms of optical absorption models. It is concluded that the threechromophore extended Beer Lambert Law model is better suited for assessing the palm and facial tissue oxygenations, especially when spectral data in the wavelengths region of [516-580]nm is used for the analysis. A first attempt of using the facial StO2 to detect and to classify people’s emotional state is initiated in this project. The objective of this work is to understand how strong emotions, such as distress that caused by mental or physical stimulations, can be detected using physiological feature such as StO2. Based on data collected from ~20 participants, it is found that the forehead StO2 is elevated upon the onset of strong emotions that triggered by mental stimulation. The StO2 pattern in the facial region upon strong emotions that are initiated by physical stimulations is quite complicated, and further work is needed for a better understanding of the interplays between bodily physique, individual’s health condition and blood transfusion control mechanism. Most of this work has already been published and future research to follow up when the author returns back to China is highlighted.
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Ellingsen, Pål Gunnar. "Polarimetric and hyperspectral imaging methods for characterisation of bio- and nanomaterials." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for fysikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-26601.
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Presented here is the development of a time gated Raman spectrometer, a new method for designing polarimeters, testing of an overdetermined Mueller matrix imaging ellipsometer and Mueller matrix imaging of collagen fibre directions and silicon strain. In addition, a new method, spectral correlation, for analysing hyperspectral images, is developed, implemented and tested. The time gated Raman spectrometer is capable of gating away long lifetime luminescence (> 2 ns), by using a time gated ICCD and a 2 ns pulsed Nd:YAG laser. Furthermore, the system can be used to study UV resonance Raman effects, due to the third (355 nm) and fourth (266 nm) order harmonic excitations available. For optimising broadband polarimeters, genetic algorithms are employed. The resulting polarimeters are demonstrated to have a lower noise propagation and a broader spectral range, compared to previous commercial and patented designs. One of the designs is realised as a Mueller matrix liquid crystal variable retarder based ellipsometer. Its performance is in good agreement with computer optimised models. For calibrating the instrument, an extended version of the eigenvalue calibration method is employed. Mueller matrix imaging is used together with state of the art decomposition methods to image collagen fibre orientations in cartilage and tendon. 3D directional imaging of the collagen fibres is achieved by using different angles of incidence upon the sample and a new method for calculating the fibre direction from the linear retardance. The method uses an effective medium approach to justify that the slow axis is along the direction of the fibres. A similar approach is used to study strain in silicon wafers. Lastly, a spectral correlation method for analysing hyperspectral fluorescence images is developed and demonstrated to be good for analysing double stained amyloid plaques in mice. The resulting analysis allows for calculating the radius dependent mean and standard deviations for a set of plaques as a function of radius. By studying amyloid plaques from mice of different ages, it was found that there were significant changes with age in the structure of the plaques. Furthermore, the method was found to work well for analysing multi-component Raman spectra, where it detected small amounts of tetrahydrofuran in water.
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September, Danwille Jacqwin Franco. "Detection and quantification of spice adulteration by near infrared hyperspectral imaging." Thesis, Stellenbosch : University of Stellenbosch, 2011. http://hdl.handle.net/10019.1/6624.
Full textAbstract:
Thesis (MSc Food Sc)--University of Stellenbosch, 2011.ENGLISH ABSTRACT: Near infrared hyperspectral imaging (NIR HSI) in conjunction with multivariate image analysis was evaluated for the detection of millet and buckwheat flour in ground black pepper. Additionally, midinfrared (MIR) spectroscopy was used for the quantification of millet and buckwheat flour in ground black pepper. These techniques were applied as they allow non-destructive, invasive and rapid analysis. Black pepper and adulterant (either millet or buckwheat flour) mixtures were made in 5% (w/w) increments spanning the range 0-100% (w/w). The mixtures were transferred to eppendorf tube holders and imaged with a sisuChema short wave infrared (SWIR) pushbroom imaging system across the spectral range of 1000–2498 nm. Principal component analysis (PCA) was applied to pseudo-absorbance images for the removal of unwanted data (e.g. background, shading effects and bad pixels). PCA was subsequently applied to the ‘cleaned’ data. An adulterant concentration related gradient was observed in principal component one (PC1) and a difference between black pepper adulterated with buckwheat and millet was noted in PC4. Four absorption peaks (1461, 2241, 2303 and 2347 nm) were identified in the loading line plot of PC1 that are associated with protein and oil. The loading line plot of PC4 revealed absorption peaks at 1955, 1999, 2136 and 2303 nm, that are related to protein and oil. Partial least squares discriminant analysis (PLS-DA) was applied to NIR HSI images for discrimination between black pepper adulterated with varying amounts of adulterant (millet or buckwheat). The model created with millet adulterated black pepper samples had a classification accuracy of 77%; a classification accuracy of 70% was obtained for the buckwheat adulterated black pepper samples. An average spectrum was calculated for each sample in the NIR HSI images and the resultant spectra were used for the quantification of adulterant (millet or buckwheat) in ground black pepper. All samples were also analysed using an attenuated total reflectance (ATR) Fourier transform (FT) – infrared (IR) instrument and MIR spectra were collected between 576 and 3999 cm-1. PLS regression was employed. NIR based predictions (r2 = 0.99, RMSEP = 3.02% (w/w), PLS factor = 4) were more accurate than MIR based predictions (r2 = 0.56, RMSEP = 19.94% (w/w), PLS factors = 7). Preprocessed NIR spectra revealed adulterant specific absorption bands (1743, 2112 and 2167 nm) whereas preprocessed MIR spectra revealed a buckwheat specific signal at 1574 cm-1. NIR HSI has great promise for both the qualitative and quantitative analysis of powdered food products. Our study signals the beginning of incorporating hyperspectral imaging in the analysis of powdered food substances and results can be improved with advances in instrumental development and better sample preparation.
AFRIKAANSE OPSOMMING: Die gebruik van naby infrarooi hiperspektrale beelding (NIR HB) tesame met veelvoudige beeldanalise is ondersoek vir die opsporing van stysel-verwante produkte (giers en bokwiet) in gemaalde swart pepper. Middel-infrarooi (MIR) spektroskopie is addisioneel gebruik vir die kwantifisering van hierdie stysel-verwante produkte in swart pepper. Albei hierdie tegnieke is toegepas aangesien dit deurdringend van aard is en dit bied nie-destruktiewe sowel as spoedige analise. Swart pepper en vervalsingsmiddel (giers of bokwiet) mengsels is uitgevoer in 5% (m/m) inkremente tussen 0 en 100% (m/m). Eppendorfbuishouers is met die mengsels gevul en hiperspektrale beelde is verkry deur die gebruik van ‘n sisuChema SWIR (kortgolf infrarooi) kamera met ‘n spektrale reikwydte van 1000–2498 nm. Hoofkomponent-analise (HK) is toegepas op pseudo-absorbansie beelde vir die verwydering van ongewenste data (bv. agtergrond, skadu en dooie piksels). Hoofkomponent-analise is vervolgens toegepas op die ‘skoon’ data. Hoofkomponent (HK) een (HK1) het die aanwesigheid van ‘n vervalsingsmiddel konsentrasie verwante gradient getoon terwyl HK4 ‘n verskil getoon het tussen swart pepper vervals met giers en bokwiet. Vier absorpsiepieke (1461, 2241, 2303 en 2347 nm) was geïdentifiseer binne die HK lading stip van HK1 wat met proteïen en olie geassosieer kon word. Die HK lading stip van HK4 het absorpsipieke by 1955, 1999, 2136 en 2303 nm aangedui wat verband hou met proteïen en olie. Parsiële kleinste waarde diskriminant-analise (PKW-DA) is toegepas op die hiperspektrale beelde vir die moontlike onderskeiding tussen swart pepper vervals met verskeie hoeveelhede vervalsingsmiddel (giers of bokwiet). ‘n Klassifikasie koers van 77% is verkry vir die model ontwikkel met giers vervalsde swart pepper terwyl die model ontwikkel met bokwiet vervalsde swarte pepper ‘n klassifikasie koers van 70% bereik het. ‘n Gemiddelde spektrum is bereken vir elke monster in die hiperspektrale beelde en die resulterende spektra is gebruik vir die kwantifisering van vervalsingsmiddels (giers of bokwiet) in gemaalde swart pepper. ‘n ATR FT-IR instrument met spektrale reikwydte van 576-3999 cm-1 is additioneel gebruik vir die analise van alle monsters. Parsiële kleinste waarde regressie is gebruik vir kwantifikasie doeleindes. NIR gebasseerde voorspellings (r2 = 0.99, RMSEP = 3.02% (m/m), PLS faktore = 4) was meer akkuraat as die MIR gebasseerde voorspellings (r2 = 0.56, RMSEP = 19.94% (m/m), PLS faktore = 7). Vooraf behandelde NIR spektra het vervalsingsmiddel verwante absorpsiepieke (1743, 2112 en 2167 nm) aangetoon terwyl vooraf behandelde MIR spektra ‘n bokwiet verwante absorpsiepiek by 1574 cm-1 aangedui het. NIR HB toon goeie potensiaal vir beide kwalitatiewe en kwantitatiewe analise van gepoeierde voedsel produkte. Ons studie kan gesien word as die begin van die inkorporasie van hiperspektrale beelding in die analise van gepoeierde voedsel material en verbeterde resulte kan verkry word deur die vordering in instrumentasie ontwikkeling en verbeterde monstervoorbereiding.
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Michael, Minto. "Radio frequency dielectric heating and hyperspectral imaging of common foodborne pathogens." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/18712.
Full textAbstract:
Doctor of PhilosophyDepartment of Food Science
Randall K. Phebus
Intervention techniques to control foodborne pathogens, and rapid identification of pathogens in food are of vital importance to ensure food safety. Therefore, the first objective of this research was to study the efficacy of radio frequency dielectric heating (RFDH) against C. sakazakii and Salmonella spp. in nonfat dry milk (NDM) at 75, 80, 85, or 90°C. Using thermal-death-time (TDT) disks, D-values of C. sakazakii in high heat (HH)- and low heat (LH)-NDM were 24.86 and 23.0 min at 75°C, 13.75 and 7.52 min at 80°C, 8.0 and 6.03 min at 85°C, and 5.57 and 5.37 min at 90°C, respectively. D-values of Salmonella spp. in HH- and LH-NDM were 23.02 and 24.94 min at 75°C, 10.45 and 12.54 min at 80°C, 8.63 and 8.68 min at 85°C, and 5.82 and 4.55 min at 90°C, respectively. The predicted (TDT) and observed (RFDH) destruction of C. sakazakii and Salmonella spp. were in agreement, indicating that the organisms' behavior was similar regardless of the heating system (conventional vs. RFDH). However, RFDH can be used as a faster and more uniform heating method for NDM to achieve the target temperatures. The second objective of this research was to study if hyperspectral imaging can be used for the rapid identification and differentiation of various foodborne pathogens. Four strains of C. sakazakii, 5 strains of Salmonella spp., 8 strains of E. coli, and 1 strain each of L. monocytogenes and S. aureus were used in the study. Principal component analysis and kNN (k-nearest neighbor) were used to develop classification models, which were then validated using a cross-validation technique. Classification accuracy of various strains within genera including C. sakazakii, Salmonella spp. and E. coli, respectively was 100%; except within C. sakazakii, strain BAA-894, and within E. coli, strains O26, O45 and O121 had 66.67% accuracy. When all strains were studied together (irrespective of their genera) for the classification, only C. sakazakii P1, E. coli O104, O111 and O145, S. Montevideo, and L. monocytogenes had 100% classification accuracy; whereas, E. coli O45 and S. Tennessee were not classified (classification accuracy of 0%).
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Ergin, Leanna N. "ENHANCED DATA REDUCTION, SEGMENTATION, AND SPATIAL MULTIPLEXING METHODS FOR HYPERSPECTRAL IMAGING." Cleveland State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=csu1501871494997272.
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Alterini, Tommaso. "Hyperspectral imaging system for the fast recording of the ocular fundus." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672138.
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Vision loss affects physical, psychological, and emotional wellbeing and social life as well. High life expectancy and health policies are translated into an aging population worldwide, which has higher risk of eye’s disorders and diseases. Therefore, new systems able to contribute at non-invasive objective diagnosis of ocular diseases are demanded. In this context, optical imaging techniques have a primary role as they allow obtaining information from almost any part of the eye. In particular, many important eye and systemic diseases early manifest themselves in the retina and, since the pioneer study of Helmholtz, many resources have been spent to acquire good images of the ocular fundus. In common clinics, fundus photography is restricted to color imaging sensors with only three spectral bands, and, due to metamerism, many structures might remain hidden. Recently, hyperspectral imaging techniques have come to view as a promising and powerful tool for the spectral analysis of several retinal diseases, increasing the amount of information extractable from fundus photography. However, in the literature, examples are restricted to the visible range of the electromagnetic spectrum, have few bands and/or make use of modified commercial fundus cameras. Accordingly, the goal of this project is to build a novel hyperspectral fundus camera based on light-emitting diodes allowing the fast imaging of the retina both in the visible and in the near infrared region of the spectrum, which has never been explored, through a considerable number of spectral bands. This fundus camera has been designed, tested and developed with new custom-made illumination and detection strategies combined with novel cutting- edge technology at the Center for Sensors, Instruments and Systems Development (CD6) of the Universitat Politècnica de Catalunya (UPC, Terrassa). Finally, after a scrupulous clinical study carried out at the Instituto de Microcirugía Ocular (IMO, Barcelona) and at the University Vision Center of UPC (CUV-UPC, Terrassa), qualitative and quantitative results are presented for healthy and diseased eyes. The available spectroscopic information and the visualization of retinal structures and lesions, especially those affecting the choroidal vasculature and retinal pigment epithelium that are hardly visible in conventional color fundus images, underline the clinical potential of this system as a new tool for ophthalmic diagnosis.La pèrdua de visió afecta el benestar físic, psicològic i emocional, i també la vida social de les persones. La llarga esperança de vida i les polítiques de salut es tradueixen en un envelliment de la població a tot el món, la qual presenta un major risc de patir trastorns i malalties oculars. Per tant, es requereixen nous sistemes capaços de contribuir al diagnòstic objectiu no invasiu d'aquestes malalties. En aquest context, les tècniques d’imatge òptica tenen un paper primordial, ja que permeten obtenir informació de gairebé qualsevol part de l’ull. En particular, moltes malalties oculars i sistèmiques importants es manifesten primerencament a la retina i, des de l’estudi pioner de Helmholtz, s’han gastat molts recursos per adquirir bones imatges del fons ocular. Tradicionalment, a les clíniques oftalmològiques, la fotografia de fons d'ull es fa amb sensors d'imatge en color amb només tres bandes espectrals i, a causa del metamerisme, algunes estructures poden romandre amagades. Recentment, les tècniques d’imatge hiperspectral s’han mostrat com una eina prometedora per a l’anàlisi espectral de la retina, augmentant la quantitat d’informació que es pot extreure de la fotografia del fons d'ull. Tanmateix, a la literatura, els exemples es restringeixen al rang visible de l’espectre electromagnètic, tenen poques bandes i/o fan ús de càmeres de fons d'ull comercials modificades. En conseqüència, l’objectiu d’aquest projecte és construir una nova càmera de fons d'ull hiperspectral basada en díodes emissors de llum que permetin obtenir una imatge ràpida de la retina tant a la regió visible com a la de l’infraroig proper de l’espectre, que mai no s’ha explorat, a través d’un nombre considerable de bandes espectrals. Aquesta càmera de fons d'ull ha estat dissenyada i desenvolupada amb noves estratègies d’il·luminació i detecció fetes a mida i combinades amb nova tecnologia d’avantguarda al Centre de Desenvolupament de Sensors, Instrumentació i Sistemes (CD6) de la Universitat Politècnica de Catalunya (UPC, Terrassa). Finalment, després de la realització d’un estudi clínic detallat portat a terme a l’Institut de Microcirurgia Ocular (IMO, Barcelona) i al Centre Universitari de la Visió de la UPC (CUV-UPC, Terrassa), en aquesta tesi es presenten resultats qualitatius i quantitatius tant per a ulls sans com amb malalties de retina. La informació espectroscòpica i la visualització d’estructures i lesions de la retina, especialment aquelles que afecten la vasculatura de coroides i a l’epiteli pigmentari de la retina que són difícilment visibles en les imatges convencionals del fons d'ull en color, posen de manifest el potencial clínic d’aquest sistema com a nova eina per al diagnòstic oftalmològic
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Huang, Hui. "Non-destructive detection of pork intramuscular fat content using hyperspectral imaging." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=119675.
Full textAbstract:
Intramuscular fat levels of pork affect the flavor of pork meat. In the pork industry, two quality attributes namely intramuscular fat (IMF) content and marbling score (MS) are used to represent intramuscular fat levels of pork meat. Conventional determination methods are not suitable for the current requirements of the pork industry as they are either destructive or subjective. This study investigated the use of hyperspectral imaging in evaluating intramuscular fat content and marbling score of pork. Intramuscular fat distribution along the longissmus muscle and the influences of freezing, thawing, and image pattern analysis on prediction capacity were also considered. Near infrared (NIR) hyperspectral imaging technique from 900 to 1700 nm was used for prediction of IMF content and MS. Fresh pork at the 3rd/4th last rib was imaged. Pattern analysis techniques of Gabor filter, wide line detector (WLD), and an improved grey-level co-occurrence matrix (GLCM) were studied and different image features, i.e. spectral, texture, and line features, were extracted. Key wavelengths were identified. Multiple linear regression (MLR) was used to develop prediction models. For determination of marbling score, the MLR model, using the first derivative of Gabor filtered mean spectra, performed best with a prediction accuracy of 0.90 at wavelengths of 961, 1186 and 1220 nm. For intramuscular fat content, prediction accuracy of 0.85 was obtained using the raw mean spectra at 1207 and 1279 nm. The distribution map of IMF content in pork was developed. The results showed the possibility of rapid and non-destructive evaluation of intramuscular fat level of pork using NIR images. Regarding marbling as a visual index, a method for objective evaluation of pork marbling score using red-green-blue (RGB) images was developed by applying WLD-based linear models. The possibility of non-destructive prediction of IMF content and MS using frozen and frozen-thawed pork was studied. Prediction accuracy of 0.90 for MS was achieved for frozen pork. Prediction accuracy of 0.82 for IMF content and accuracy of 0.91 for MS were realized by frozen-thawed pork. The potential of frozen and frozen-thawed pork for assessment of marbling score and frozen-thawed pork for the assessment of intramuscular fat content were demonstrated. Besides the effects of freezing and thawing, the variation of IMF content and MS across the last seven thoracic longissmus muscle was studied. Relationships between IMF content and MS at the last rib and the corresponding attribute at other ribs and the whole section of the loin were determined. The relationship between NIR images of rib end and the IMF level of pork at the six last thoracic ribs was investigated. Close relationships were indicated, especially between the images of rib end and IMF levels at the 2nd/3rd last ribs and the 2nd last/last ribs.La teneur en matières grasses du porc affecte la saveur de la viande de porc. Dans l'industrie porcine, la graisse intramusculaire (GIM) et la cote de persillage (CP) sont deux propriétés qui déterminent la teneur en gras du porc. Les méthodes conventionnelles de détermination ne sont pas adaptées aux besoins actuels de l'industrie car elles sont destructrices ou subjectives. Cette étude porte sur l'utilisation de l'imagerie hyperspectrale dans l'évaluation de la teneur en graisse intramusculaire et du persillage du porc. Les effets de la répartition de la graisse intramusculaire le long du muscle Longissmus, de la congélation, du dégel et de l'analyse de la forme pour le traitement de l'image ont été pris en compte. Une technique d'imagerie hyperspectrale proche infrarouge (IR) allant de 900 à 1700 nm a été utilisée pour prédire le GIM ou la CP. La viande fraîche au niveau de la 3ème/4ème côte du porc a été utilisée pour recueillir les images hyperspectrales. Des analyses de la forme fondée sur les techniques du filtre de Gabor, du détecteur linéaire à large spectre (WLD) et de la matrice de cooccurrence de niveau gris améliorée (GLCM) ont été étudiées et les propriétés de l'image, i.e spectre, texture et propriétés des lignes, ont été extraites. La régression linéaire multiple (RLM) a été utilisée pour développer des modèles de prédiction. Pour la cote persillage, le modèle de RLM utilisant la moyenne de spectre filtrée pour la première dérivée de Gabor a le mieux performé avec une précision de calibration de 0,90 aux longueurs d'onde de 961, 1186 et 1220 nm. Pour le GIM, une précision de calibration de 0.85 a été obtenue avec un spectre moyen de base à 1207 et 1279 nm. La distribution du contenu de GIM a été illustrée. Les résultats démontrent la possibilité d'utiliser les images hyperspectralces proche IR pour évaluer rapidement et de façon non-destructive le taux de gras intramusculaire du porc. En ce qui concerne le persillage en tant qu'indice visuel, une méthode objective d'évaluation de la cote persillage utilisant des images rouge-vert-bleu (RGB) a été développée en appliquant un WLD basé sur un model linéaire au canal vert. La possibilité d'un contrôle non-destructif du GIM et de la CP utilisant du porc congelé et décongelé a été étudiée. Une précision de la prédiction de 0.90 pour la CP a été réalisée avec du porc congelé. Une précision de la prédiction de 0.82 pour le GIM découle du porc décongelé. Le potentiel du porc congelé et décongelé pour l'évaluation de la cote de persillage et du porc décongelé pour l'évaluation de la teneur en gras intramusculaire a été démontré. Outre l'effet du gel et du dégel, la variation du GIM et de la CP à travers les sept derniers muscles thoraciques Longissmus a été étudiée. Les relations entre le GIM et la CP à la dernière côte et les propriétés correspondantes aux autres côtes et au filet ont été déterminées avec précision. La relation entre les images de proche IR à l'extrémité et le niveau de GIM du porc six dernières côtes thoraciques a été étudiée. Des relations étroite ont été déterminées, en particulier entre les images de l'extrémité de la côte et les taux de GIM aux 2eme/3eme dernières côtes et la 2eme dernière côte.
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Chen, He. "Microscopic Hyperspectral Image Analysis via Deep Learning." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/396188.
Full textAbstract:
Hyperspectral imaging (HSI) is a technique that can obtain more spectral information than that in normal color images. Due to this property and strength in material classification, it is widely used in remote sensing, agriculture, and environmental monitoring. In recent years, with the rapid developments of hardware, hyperspectral cameras have become more portable and a ordable. An increasing number of studies are being conducted on HSI systems, and research focuses have expanded from remote sensing to close-range objects. With a proper microscopic kit, a hyperspectral camera can capture images of objects of micrometers in size.
In this thesis, an HSI system is introduced which consists of a hyperspectral camera, a microscope, control software, and an image processing workstation. The samples are placed under the microscope which has the camera mounted on the top. The parameters of the camera can be tuned by the control software to have the best image quality. After the setup, the camera takes the HSI image of the samples. Then, the image is transferred to the workstation and saved as a raw HSI image for further process. Two datasets of cells and microplastics are collected and introduced as benchmark datasets for this research.
The reason to build these two benchmarks is because of their demands. In the area of cell viability assay, traditional methods use
uorescent dyes to distinguish live and dead cells. Although working very reliably, they require physical contact with the cells, which a ects the appearance of the cells and some of the original cell features. As a consequence, there is a demand for the development of non-invasive technology for cell analysis. Our HSI system is capable of using computer vision techniques to classify live and dead cells as a non-invasive and systematic method so that the property of the cells can remain unchanged and the system can be operated without special skills. The microplastics dataset is built to address the needs of environmental protection which is an important research topic with significant social and economic values. The increasing amount of microplastics in the ocean has attracted enormous concern because of its potential to damage the ecosystem and a ect the health of humans and animals. While HSI has shown great potential in analyzing microplastics, studies in this direction are hindered by the lack of public available image data. Therefore, there is an urgent so that there is an urgent demand to build a dataset for microplastics detection.
After the datasets have been constructed, we evaluate the support vector machine (SVM) on them for the baseline approach. We apply several feature extraction methods to process the HSI images of the cells before feeding them into the SVM, including extended morphology profile (EMP), tensor morphology profile (TMP), 3D scale-invariant feature transform (SIFT3D), 3D local derivative pattern (3DLDP) and spectral-spatial scaleinvariant feature transform (SS-SIFT). Among them, TMP has the best performance for the cell classification task. Regarding the detection of microplastics, the spectral signature is used to extract the feature and is fed into SVM for detection.
Furthermore, we propose a novel attention-based convolutional neural networks (CNN) to classify the cells to take advantage of the development in deep learning. Inspired by the VGG networks, we first build a classification network for our hyperspectral data. Then, a band weighting network and a spatial weighting network are integrated into the backbone. The band weighting network assigns a weight to each band in the hyperspectral images. The weights can suppress redundant bands that do not make an important contribution to the classification task and make the classification network focus on the bands that have more important features for classification. The spatial weighting network assigns a weight to each pixel in the hyperspectral images. The weights can help the classification network focus on important parts of the images and ignore the irrelevant parts. These two weighting networks help to improve the final classification accuracy of the cells.
In the experiments on hand-crafted features, SVM with TMP feature extraction method has the best accuracy of 83.72% for the cell classification task. SVM with spectral signature produces 99.13% accuracy on the microplastics detection task. In comparison, the attention-based CNN achieves 98.17% for the cell classification task. These results show that our HSI system and classification methods have great potential for these two classification and detection tasks. The richness of spectral information that is provided by hyperspectral images has a great potential in material recognition tasks, helping to classify di erent materials based on their unique spectral signatures of each material. Because of this, our research can contribute to a wider range of biomedical and environmental domains.Thesis (Masters)
Master of Philosophy (MPhil)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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Weekley, Jonathan Gardner. "Multispectral Imaging Techniques for Monitoring Vegetative Growth and Health." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/35738.
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Electromagnetic radiation reflectance increases dramatically around 700 nm for vegetation. This increase in reflectance is known as the vegetation red edge. The NDVI (Normalized Difference Vegetation index) is an imaging technique for quantifying red edge contrast for the identification of vegetation. This imaging technique relies on reflectance values for radiation with wavelength equal to 680 nm and 830 nm. The imaging systems required to obtain this precise reflectance data are commonly space-based; limiting the use of this technique due to satellite availability and cost.
This thesis presents a robust and inexpensive new terrestrial-based method for identifying the vegetation red edge. This new technique does not rely on precise wavelengths or narrow wavelength bands and instead applies the NDVI to the visible and NIR (near infrared) spectrums in toto.
The measurement of vegetation fluorescence has also been explored, as it is indirectly related to the efficiency of photochemistry and heat dissipation and provides a relative method for determining vegetation health.
The imaging methods presented in this thesis represent a unique solution for the real time monitoring of vegetation growth and senesces and the determination of qualitative vegetation health. A single, inexpensive system capable of field and greenhouse deployment has been developed. This system allows for the early detection of variations in plant growth and status, which will aid production of high quality horticultural crops.Master of Science
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Barberio, Manuel. "Real-time intraoperative quantitative assessment of gastrointestinal tract perfusion using hyperspectral imaging (HSI)." Thesis, Strasbourg, 2019. http://www.theses.fr/2019STRAJ120.
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La fistule anastomotique (FA) est une complication grave de la chirurgie. Une perfusion locale adéquate est fondamentale pour réduire le risque de FA. Cependant, les critères cliniques ne sont pas fiables pour évaluer la perfusion intestinale. À cet égard, l'angiographie par fluorescence (AF) a été explorée. Malgré des résultats prometteurs dans les essais cliniques, l'évaluation de l'AF est subjective, d'où l'incertitude quant à son efficacité. L'AF quantitative a déjà été introduite. Cependant, elle est limitée par la nécessité d'injecter un fluorophore. L'imagerie hyperspectrale (HSI) est une technique d'imagerie optique prometteuse couplant un spectroscope à une caméra photo, permettant une analyse quantitative des tissus en temps réel et sans contraste. L'utilisation intraopératoire de l'HSI est limitée par la présence d'images statiques. Nous avons développé la hyperspectral-based enhanced reality (HYPER), pour permettre une évaluation précise de la perfusion intraopératoire. Cette thèse décrit les étapes du développement et de la validation d'HYPERAnastomotic leak (AL) is a severe complication in surgery. Adequate local perfusion is fundamental to promote anastomotic healing, reducing the risk of AL. However, clinical criteria are unreliable to evaluate bowel perfusion. Consequently, a tool allowing to objectively detect intestinal viability intraoperatively is desirable. In this regard, fluorescence angiography (FA) has been explored. In spite of promising results in clinical trials, FA assessment is subjective, hence the efficacy of FA is unclear. Quantitative FA has been previously introduced. However, it is limited by the need of injecting a fluorophore. Hyperspectral imaging (HSI) is a promising optical imaging technique coupling a spectroscope with a photo camera, allowing for a contrast-free, real-time, and quantitative tissue analysis. The intraoperative usability of HSI is limited by the presence of static images. We developed hyperspectral-based enhanced reality (HYPER), to allow for precise intraoperative perfusion assessment. This thesis describes the steps of the development and validation of HYPER
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Laborde, Antoine. "Detection of minor compounds in food powder using near infrared hyperspectral imaging." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASB017.
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L’imagerie hyperspectrale proche infrarouge (PIR) permet d’obtenir une carte spectrale d’un échantillon organique. La mesure d’un spectre pour chaque pixel de la caméra permet notamment la recherche de composés minoritaires dans les poudres agroalimentaires. Cependant, l’analyse spectrale PIR est limitée à une couche de profondeur donnée. De plus, la taille des particules associée à une résolution insuffisante des caméras PIR actuelles induisent un mélange des signaux spectraux dans les pixels de l’image. Ces deux problèmes sont une limitation pour l’analyse des composés minoritaires dans les poudres agroalimentaires.Nous proposons une méthode permettant de déterminer la profondeur de détection d’une cible composite placée dans un produit pulvérulent tel que la farine de blé. Basée sur une régression par projection sur les structures latentes, cette méthode permet d’appréhender l’atténuation du signal PIR lorsque la couche de poudre augmente, et ce malgré les problèmes inhérents à la détection en profondeur.De plus, deux stratégies de démélange de spectres sont proposées dans le but de détecter les pixels contenant des signatures de particules minoritaires. Le manque de valeur de référence utilisées en tant que données de validation des algorithmes ainsi que l’ambiguïté des spectres des composés pures à démélanger sont deux difficultés majeures. Une première stratégie consiste à modélisation la variabilité des spectres étudiés via l’Analyse en Composantes Principales afin de construire un algorithme de détection performant. La deuxième stratégie, basée sur la Multivariate Curve Resolution Alternating Least-Squares permet le démélange des signaux par pixels dans un cas plus complexeNear-infrared (NIR) hyperspectral imaging provides a spectral map for organic samples. Minor compounds in food powder can be looked for by analyzing the pixel spectra. However, the NIR spectral analysis is limited to a given depth. Besides, particles smaller than the pixel size induce a mixed spectral signature in the pixels. These two issues are an obstacle to the analysis of minor compounds in food powders.We propose a method to determine the detection depth of a composite target under a layer of powder such as wheat flour. It is based on the Partial Least Squares regression and provides an understanding of how the NIR signal is attenuated when the layer of powder despite the penetration depth issues.Two spectral unmixing strategies are proposed to detect pixel with minor compound NIR signatures. The lack of reference values to validate the model and the ambiguity of the spectral signature to unmix are two major difficulties. The first method models the spectral variability using Principal Component Analysis to design a performant detection algorithm. Then, for a more complex situation, the Multivariate Curve Resolution Alternating Least-Squares algorithm is used to unmix each pixel
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Zhang, Xin. "Application of chemometrics to hyperspectral imaging analysis of environmental and agricultural samples." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/301275.
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This Thesis deals with the resolution of hyperspectral imaging data by using chemometric methods, in particular by using appropriate data pretreatment methods and by using Multivariate Curve Resolution (MCR) methods. The main contribution of the present Thesis is the study and implementation of the MCR-ALS (Multivariate Curve Resolution Alternating Least Squares) method for the resolution of hyperspectral images, collected by remote sensing (airborne or space borne Earth observation instrument) and by micro-spectroscopy imaging. Specifically, in this Thesis work, we explore the combination of chemometric and hyperspectral imaging methods for the resolution of spectra (signatures) and spatial distribution maps of the chemical constituents of a sample. The ultimate goal of this study is to improve the analysis and interpretation of hyperspectral imaging data by taking advantage of different chemometric powerful tools. Local rank/selectivity properties describing the spatial information of spectroscopic images can be used as a constraint to increase the performance of MCR methods significantly, decreasing rotation ambiguity uncertainties. Different multivariate resolution methods were compared, such as MCR-ALS, Principal Component Analysis (PCA), and Minimum Volume Simplex Analysis (MVSA), Multivariate Curve Resolution-Function Minimization (MCR-FMIN), MCR-BANDS and FAC-PACK. All these approaches have been used for the evaluation of the extension of rotation ambiguities remaining in the results after their application. Several hyperspectral images provided by standard and widely used instruments such as NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS), Raman hyperspectral imaging Spectrometer, and Infrared hyperspectral imaging Spectrometer have been used as example of data sets to test the different methods, in particular to test the MCR-ALS method.
The results obtained in this Thesis show that MCR-ALS method can be successfully used for hyperspectral image resolution purposes. The spectra signatures of the pure constituents present in hyperspectral images and their concentration distribution at a pixel level can be estimated. Constituents identification can be performed using the resolved pure spectra signatures and comparing them to reference spectra from spectral libraries or from experimental spectra of reference samples. Application of image data pretreatment methods reduce significantly the presence of strong fluorescence background in Raman hyperspectral images. In contrast, infrared hyperspectral imaging is not affected by fluorescence. Kramers-Kronig transform enables to calculate absorption spectra in case only reflectance spectra can be measured for infrared spectra. The extent of rotation ambiguity associated to MCR-ALS and other resolution methods can be rather high when they are applied for hyperspectral image resolution with high noise. The correct resolution of hyperspectral images can only be guaranteed if additional constraints are applied, such as those providing information about the local rank properties of the image, i.e. about the presence or absence of the different constituents (components) in the image pixels. Only in this way it is possible to increase the reliability of the solutions provided by MCR methods and decrease the uncertainties associated to them. Appropriate use of local rank and selectivity constraints can improve significantly the quality of the pure spectra (signatures) and of the constituent distribution maps resolved by MCR-ALS analysis of hyperspectral images in remote sensing studies. Use of correlation coefficients between selected spectra and image pixel spectra is shown to provide an alternative way for the application of the selectivity constraint in hyperspectral images for the first time. This alternative method resulted to be satisfactory when pure pixels exist. MCR-BANDS method can be used to get estimations of the extension of rotation ambiguities in MCR resolved results. The Area of Feasible Solutions represents feasible solutions geometrically. The range of rotation ambiguity calculated by MCR-BANDS and AFS are in agreement. MCR-ALS with the trilinearity constraint is an effective way to characterize and resolve Excitation-Emission Matrix fluorescence spectra (EEM).Esta Tesis trata de la resolución de datos de imágenes hiperespectrales utilizando métodos quimiométricos, en particular mediante el uso de métodos de pretratamiento de datos y utilizando métodos de resolución multivariante de curvas (MCR). La principal contribución de la presente Tesis es el estudio y la aplicación del método MCR-ALS (resolución multivariante de curvas mediante mínimos cuadrados alternados) para la resolución de imágenes hiperespectrales, adquiridas mediante técnicas de teledetección y mediante técnicas de micro-espectroscopia. Específicamente, en el trabajo de esta Tesis, se explora la combinación de los métodos quimiométricos y de los métodos de análisis de imágenes hiperespectrales, para la resolución de los espectros (firmas) y de los mapas de distribución de los componentes químicos de la muestra. El objetivo final de este estudio es mejorar el análisis y la interpretación de los datos de imágenes hiperespectrales mediante el aprovechamiento de diferentes herramientas quimiométricas poderosas. La detección del rango local y las propiedades de selectividad que describen la información espacial de los componentes presentes en las imágenes espectroscópicas. Se han comparado diferentes métodos de resolución, tales como MCR-ALS, MVSA (Mínimo Volumen Simplex Análisis), PCA (Análisis de Componentes Principales), y MCR-FMIN. Los métodos MCR-BANDS y FAC-PACK se han utilizado para la evaluación de la extensión de las ambigüedades rotacionales existentes en los resultados después de la aplicación de estos métodos de resolución multivariante. En esta Tesis se han analizado diversos conjuntos de datos compuestos por varias imágenes hiperespectrales proporcionadas por instrumentos estándar tales como el espectrómetro de imágenes hiperespectrales en el visible y en el infrarrojo AVIRIS de la NASA, y diversos espectrómetros de imágenes hiperespectrales Raman y infrarrojo de laboratorio. La eficacia del procedimiento MCR-ALS se ilustra proporcionando comparaciones exhaustivas con otros métodos de resolución de mezclas espectrales a partir de conjuntos de datos hiperespectrales simulados y reales.
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Gevaux, Lou. "3D-hyperspectral imaging and optical analysis of skin for the human face." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSES035.
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L’imagerie hyperspectrale (HSI), une méthode non invasive permettant de mesurer in vivo la réflectance spectrale, a démontré son fort potentiel pour l’analyse des propriétés optiques de la peau pour des zones planes et de petite taille : l’association d’un modèle optique de peau, d’une modélisation de ses interactions avec la lumière et d’une méthode d’optimisation permet d’analyser l’image hyperspectrale en chaque pixel et d’estimer des cartographies de concentrations en chromophores, comme la mélanine et le sang. Le but de ce travail est l’extension de la méthode pour la mesure et l’analyse de surfaces larges et non planes, et en particulier du visage humain. Les mesures d’objets complexes comme le visage sont affectées par des variations spatiales d’éclairement, que l’on appelle dérives d’éclairement. A moins d’être prises en compte dans le modèle, celles-ci créent des erreurs dans l’analyse des images.Nous proposons en 1ère partie de ce travail une caméra HSI grand-champ (acquisition de bandes spectrales de 10 nm de largeur entre 400 et 700 nm), combinée avec un système d’acquisition de la géométrie 3D par projection de franges. Une acquisition courte étant cruciale in vivo, un compromis entre résolution et vitesse d’acquisition permet un temps d’acquisition inférieur à 5 secondes.La caméra HSI a été associée avec un scanner 3D afin de corriger les dérives d’éclairement en utilisant la géométrie 3D et des principes de radiométrie. L’éclairement reçu par le visage est calculé en chaque pixel puis utilisé pour supprimer les dérives d’éclairement dans l’image hyperspectrale, un prétraitement à appliquer avant l’analyse. Cependant, cette méthode n’est pas satisfaisante sur les zones du visage pratiquement perpendiculaires à l’axe optique de la caméra, comme les côtés du nez, et a été rejetée en faveur d’un algorithme d’optimisation robuste aux dérives d’éclairement dans la méthode d’analyse.L’analyse de la peau à partir des images hyperspectrales est basée sur l’utilisation de modèles optiques. La peau est modélisée par un matériau translucide à deux couches dont les propriétés d’absorption dépendent de sa composition en chromophores. Les interactions lumière-peau sont modélisées à l’aide d’une approche à deux flux. La résolution d’un problème inverse par optimisation permet d’estimer la composition en chromophores à partir de la réflectance spectrale mesurée. Les modèles optiques choisis sont un bon compromis entre une description fidèle de la peau et un temps de calcul acceptable, qui augmente de manière exponentielle avec le nombre de paramètres du modèle. Les cartes de chromophores estimées peuvent être affichées sous forme 3D grâce à l’information mesurée par la caméra HSI-3D.Un point faible de la méthode est le manque d’information sur les propriétés de diffusion de la peau, considérées identiques d’une personne à l’autre et d’une partie du corps à l’autre. Dans la 2nd partie de ce travail, nous utilisons le projecteur de franges initialement dédié à l’acquisition 3D, pour mesurer la fonction de transfert de modulation (FTM) de la peau, qui fournit de l’information sur l’absorption et la diffusion. La FTM est mesurée par imagerie dans le domaine fréquentiel spatial (SFDI) et analysée avec l’équation de la diffusion pour estimer le coefficient de diffusion de la peau. Sur des objets non-plats, l’extraction d’information indépendamment des dérives d’éclairement est un défi important. L’originalité de la méthode proposée repose sur l’association de la HSI et SFDI dans le but d’estimer des cartes de coefficient de diffusion sur le visage indépendamment de sa forme.Nous insistons sur l’importance d’une acquisition courte pour des mesures in vivo, cependant, l’analyse par optimisation demande plusieurs heures de calcul. L’utilisation des réseaux de neurones comme alternative à l’optimisation nous semble prometteur, des premiers résultats ayant montré une forte réduction du temps de calcul, d’environ 1 heure à 1 secondeHyperspectral imaging (HSI), a non-invasive, in vivo imaging method that can be applied to measure skin spectral reflectance, has shown great potential for the analysis of skin optical properties on small, flat areas: by combining a skin model, a model of light-skin interaction and an optimization algorithm, an estimation of skin chromophore concentration in each pixel of the image can be obtained, corresponding to quantities such as melanin and blood. The purpose of this work is to extend this method to large, non-flat areas, in particular the human face. The accurate measurement of complex objects such as the face must account for variances of illumination that result from the 3D geometry of an object, which we call irradiance drifts. Unless they are accounted for, irradiance drifts will lead to errors in the hyperspectral image analysis.In the first part of the work, we propose a measurement setup comprising a wide field HSI camera (with an acquisition range of 400 - 700 nm, in 10 nm width wavebands) and a 3D measurement system using fringe projection. As short acquisition time is crucial for in vivo measurement, a trade-off between resolution and speed has been made so that the acquisition time remains under 5 seconds.To account for irradiance drifts, a correction method using the surface 3D geometry and radiometry principles is proposed. The irradiance received on the face is computed for each pixel of the image, and the resulting data used to suppress the irradiance drifts in the measured hyperspectral image. This acts as a pre-processing step to be applied before image analysis. This method, however, failed to yield satisfactory results on those parts of the face almost perpendicular to the optical axis of the camera, such as the sides of the nose, and was therefore discarded in favor of using an optimization algorithm robust to irradiance drifts in the analysis method.Skin analysis from the measured hyperspectral image is performed using optical models and an optimization method. Skin is modeled as a two-layer translucent material whose absorption and scattering properties are determined by its composition in chromophores. Light-skin interactions are modeled using a two-flux method. An inverse problem is solved by optimization to retrieve information about skin composition from the measured reflectance. The chosen optical models represent a trade-off between accuracy and acceptable computation time, which increases exponentially with the number of parameters in the model. The resulting chromophore maps can be added to the 3D mesh measured using the 3D-HSI camera for display purposes.In the spectral reflectance analysis method, skin scattering properties are assumed to be the same for everyone and on every part of the body, which represents a shortcoming. In the second part of this work, the fringe projector originally intended for measuring 3D geometry is used to acquire skin modulation transfer function (MTF), a quantity that yields information about both skin absorption and scattering coefficients. The MTF is measured using spatial frequency domain imaging (SFDI) and analyzed by an optical model relying on the diffusion equation to estimate skin scattering coefficients. On non-flat objects, retrieving such information independently from irradiance drifts is a significant challenge. The novelty of the proposed method is that it combines HSI and SFDI to obtain skin scattering coefficient maps of the face independently from its shape.We emphasize throughout this dissertation the importance of short acquisition time for in vivo measurement. The HSI analysis method, however, is extremely time-consuming, preventing real time image analysis. A preliminary attempt to address this shortcoming is presented, using neural networks to replace optimization-based analysis. Initial results of the method have been promising, and could drastically reduce calculation time from around an hour to a second
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Wendel, Alexander. "Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/19745.
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This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy.
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Sigar, Joseph Aduol. "Visible hyperspectral imaging for predicting intra-muscular fat content from sheep carcasses." Thesis, Sigar, Joseph Aduol (2020) Visible hyperspectral imaging for predicting intra-muscular fat content from sheep carcasses. Honours thesis, Murdoch University, 2020. https://researchrepository.murdoch.edu.au/id/eprint/54744/.
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Intramuscular fat (IMF) content plays a key role in the quality attributes of meat, such as sensory properties and health considerations. The tenderness, flavour and juiciness of meat are examples of sensory attributes influenced by IMF content. Traditionally, IMF content in meat was determined using destructive, time consuming and at times unsuitable methods in industry applications. However, with recent advancement of technology, there has been an interest in exlporing ways to ascertain meat quality without damage. Hyperspectral imaging analysis is an emerging technology that combines the use of spectroscopy and computer imaging analysis to obtain both the spectral and spatial information of objects of interest. Hyperspectral imaging was initially developed for remote sensing, but has recently emerged as powerful tool for non-destructive analysis of quality in the food industry and has had very accurate results in the prediction of meat qualities such as IMF content. In this thesis, we use a data set of 101 hyperspectral images of sheep carcasses to investigate the ability of multivariate statistical methods to accurately predict IMF content.
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Poon, Phillip, and Matthew Dunlop. "Calibration of High Dimensional Compressive Sensing Systems: A Case Study in Compressive Hyperspectral Imaging." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579668.
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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NVCompressive Sensing (CS) is a set of techniques that can faithfully acquire a signal from sub- Nyquist measurements, provided the class of signals have certain broadly-applicable properties. Reconstruction (or exploitation) of the signal from these sub-Nyquist measurements requires a forward model - knowledge of how the system maps signals to measurements. In high-dimensional CS systems, determination of this forward model via direct measurement of the system response to the complete set of impulse functions is impractical. In this paper, we will discuss the development of a parameterized forward model for the Adaptive, Feature-Specific Spectral Imaging Classifier (AFSSI-C), an experimental compressive spectral image classifier. This parameterized forward model drastically reduces the number of calibration measurements.