Dissertations / Theses on the topic 'Assessment of image'
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Verhaaren, Catharine C. "Improving Course Assessments Through a Product Assessment Template." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2338.pdf.
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Šmida, Vladimír. "Fingerprint Image Quality Assessment." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237090.
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Kritickým prvkem biometrického systému pro rozpoznávání otisků prstů je proces snímání. Kvalita snímku totiž ovlivňuje všechny další části systému počínaje zpracováním obrazu, přes extrakci rysů až po samotné rozhodnutí. Přestože bylo navrženo několik metod určování kvality snímku, chybějící formální specifikace kvality otisku nedovoluje ověřit jejich přesnost. Tato diplomová práce se zabývá hodnocením metod určujících kvalitu biometrického signálu otisku prstu. Popisuje jednotlivé faktory ovlivňující kvalitu spolu se současnými přístupy používanými pro její odhad. V práci je rovněž vysvětlena evaluační technika navržená za účelem porovnání schopnosti jednotlivých metod předpovědět výkon biometrického systému. Několik metod pro odhad kvality bylo implementováno a ohodnoceno touto technikou.
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Cheng, Wu. "Corrupted Image Quality Assessment." University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1335969249.
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Cui, Lei. "Topics in image recovery and image quality assessment /Cui Lei." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/368.
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Image recovery, especially image denoising and deblurring is widely studied during the last decades. Variational models can well preserve edges of images while restoring images from noise and blur. Some variational models are non-convex. For the moment, the methods for non-convex optimization are limited. This thesis finds new non-convex optimizing method called difference of convex algorithm (DCA) for solving different variational models for various kinds of noise removal problems. For imaging system, noise appeared in images can show different kinds of distribution due to the different imaging environment and imaging technique. Here we show how to apply DCA to Rician noise removal and Cauchy noise removal. The performance of our experiments demonstrates that our proposed non-convex algorithms outperform the existed ones by better PSNR and less computation time. The progress made by our new method can improve the precision of diagnostic technique by reducing Rician noise more efficiently and can improve the synthetic aperture radar imaging precision by reducing Cauchy noise within. When applying variational models to image denoising and deblurring, a significant subject is to choose the regularization parameters. Few methods have been proposed for regularization parameter selection for the moment. The numerical algorithms of existed methods for parameter selection are either complicated or implicit. In order to find a more efficient and easier way to estimate regularization parameters, we create a new image quality sharpness metric called SQ-Index which is based on the theory of Global Phase Coherence. The new metric can be used for estimating parameters for a various of variational models, but also can estimate the noise intensity based on special models. In our experiments, we show the noise estimation performance with this new metric. Moreover, extensive experiments are made for dealing with image denoising and deblurring under different kinds of noise and blur. The numerical results show the robust performance of image restoration by applying our metric to parameter selection for different variational models.
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Zhang, Wei. "Visual saliency in image quality assessment." Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/100239/.
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Advances in image quality assessment have shown the benefits of modelling functional components of the human visual system in image quality metrics. Visual saliency, a crucial aspect of the human visual system, is increasingly investigated recently. Current applications of visual saliency in image quality metrics are limited by our knowledge on the relation between visual saliency and quality perception. Issues regarding how to simulate and integrate visual saliency in image quality metrics remain. This thesis presents psychophysical experiments and computational models relevant to the perceptually-optimised use of visual saliency in image quality metrics. We first systematically validated the capability of computational saliency in improving image quality metrics. Practical guidance regarding how to select suitable saliency models, which image quality metrics can benefit from saliency integration, and how the added value of saliency depends on image distortion type were provided. To better understand the relation between saliency and image quality, an eye-tracking experiment with a reliable experimental methodology was first designed to obtain ground truth fixation data. Significant findings on the interactions between saliency and visual distortion were then discussed. Based on these findings, a saliency integration approach taking into account the impact of distortion on the saliency deployment was proposed. We also devised an algorithm which adaptively incorporate saliency in image quality metrics based on saliency dispersion. Moreover, we further investigated the plausibility of measuring image quality based on the deviation of saliency induced by distortion. An image quality metric based on measuring saliency deviation was devised. This thesis demonstrates that the added value of saliency in image quality metrics can be optimised by taking into account the interactions between saliency and visual distortion. This thesis also demonstrates that the deviation of fixation deployment due to distortion can be used as a proxy for the prediction of image quality.
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Ivkovic, Goran. "An Algorithm for Image Quality Assessment." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000049.
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CORCHS, SILVIA ELENA. "Image quality assessment for Digital documents." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/50461.
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This thesis focuses on No Reference (NR) methods for Image Quality Assessment (IQA).
A review of the IQA field is presented in Chapter 2; where the different IQA methods are described and classified.
In particular, the application of IQA methods within a workflow chain is discussed.
In Chapter 3 we focus on NR metrics for JPEG-blockiness and noise artifacts. It is in general assumed that subjective methods produce an actual estimate of the perceived quality while objective methods produce values that should be correlated with human perceptions as best as possible. From the analysis of the regression curves that correlate objective and subjective data we have found that in some cases the metric's predictions are not in correspondence with the subjective scores.
After reviewing the available databases, we realize that the distortion ranges considered are not in general representative of real case applications. Therefore, in Chapter 4 the Imaging and Vision Lab (IVL) database is introduced. It was generated with the aim of assessing the quality of images corrupted by JPEG and noise.
In Chapter 5 we approach the NR-IQA field by focusing on a classification problem. A framework based on machine learning classification is proposed that let us evaluate how images can be classified within different groups or classes, according to their quality. NR metrics are considered as features and the assigned classes are obtained from the psychovisual data. For the JPEG distortion case, the feature space of the classifiers is built using each NR metric as single feature and also a pool of eleven NR metrics.
Classification within five and three classes was addressed. In the former case, the five classes are in correspondence to the five categories recommended by the ITU (excellent, good, fair, poor, and bad) when designing image quality experiments. In the latter case we were interested in classifying images as high, medium or low quality ones. The classifiers are trained and tested on different databases. The classifier obtained using the pool of metrics outperforms each single metric classifier. Better performance is obtained in the case of three classes.
Considering an image as the combining of two signals, content and distortion, we note that the crosstalk between both signals influences both subjective and objective quality assessment. We address this problem in Chapter 6 where our working hypothesis is that regression can be improved if performed within a group of images that present similar contents in terms of low level features. The criteria chosen to divide the images in different groups is the image complexity. The proposed strategy consists on two steps: the images (of a given database) are first classified in three groups of low, medium and high complexity. In a second step, regression is performed within each of these groups separately. The strategy is tested for different NR metrics for JPEG-blockiness and noise artifacts, different databases are considered. Correlation coefficients are computed and statistical significance tests are applied. The gain in performance depends on the metric and distortion considered.
Summarizing, the two main proposals of this research work, i.e. the classification approach that combines several NR metrics and the grouping strategy, are able to outperform the correlation between subjective and objective data for the case of JPEG-blockiness. Both strategies can be extended to consider other type of distortions.
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Rousselot, Maxime. "Image quality assessment of High Dynamic Range and Wide Color Gamut images." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S034/document.
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Ces dernières années, les technologies d’écran se sont considérablement améliorées. Par exemple, le contraste des écrans à plage dynamique élevée (HDR) dépasse de loin la capacité d’un écran conventionnel. De plus, un écran à gamut de couleur étendu (WCG) peut couvrir un espace colorimétrique plus grand que jamais. L'évaluation de la qualité de ces nouveaux contenus est devenue un domaine de recherche actif, les métriques de qualité SDR classiques n'étant pas adaptées. Cependant, les études les plus récentes négligent souvent une caractéristique importante: les chrominances. En effet, les bases de données existantes contiennent des images HDR avec un gamut de couleur standard, négligeant ainsi l’augmentation de l’espace colorimétrique due au WCG et les artefacts chromatiques. La plupart des mesures de qualité HDR objectives non plus ne prennent pas en compte ces artefacts. Pour surmonter cette problématique, dans cette thèse, nous proposons deux nouvelles bases de données HDR/WCG annotés avec des scores subjectifs présentant des artefacts chromatique réaliste. En utilisant ces bases de données, nous explorons trois solutions pour créer des métriques HDR/WCG: l'adaptation des métrics de qualité SDR, l’extension colorimétrique d’une métrique HDR connue appelée HDR-VDP-2 et, enfin, la fusion de diverses métriques de qualité et de features colorimétriques. Cette dernière métrique présente de très bonnes performances pour prédire la qualité tout en étant sensible aux distorsions chromatiquesTo improve their ability to display astonishing images, screen technologies have been greatly evolving. For example, the contrast of high dynamic range rendering systems far exceed the capacity of a conventional display. Moreover, a Wide Color gamut display can cover a bigger color space than ever. Assessing the quality of these new content has become an active field of research as classical SDR quality metrics are not adapted. However, state-of-the-art studies often neglect one important image characteristics: chrominances. Indeed, previous databases contain HDR images with a standard gamut thus neglecting the increase of color space due to WCG. Due to their gamut, these databases are less prone to contain chromatic artifacts than WCG content. Moreover, most existing HDR objective quality metrics only consider luminance and are not considering chromatic artifacts. To overcome this problematic, in this thesis, we have created two HDR / WCG databases with annotated subjective scores. We focus on the creation of a realistic chromatic artifacts that can arise during compression. In addition, using these databases, we explore three solutions to create HDR / WCG metrics. First, we propose a method to adapt SDR metrics to HDR / WCG content. Then, we proposed an extension of a well-known HDR metric called HDR-VDP-2. Finally, we create a new metric based on the merger of various quality metric and color features. This last metric presents very good performance to predict quality while being sensitive to chromatic distortion
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Bjurström, Håkan, and Jon Svensson. "Assessment of Grapevine Vigour Using Image Processing." Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1342.
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This Master’s thesis studies the possibility of using image processing as a tool to facilitate vine management, in particular shoot counting and assessment of the grapevine canopy. Both are areas where manual inspection is done today. The thesis presents methods of capturing images and segmenting different parts of a vine. It also presents and evaluates different approaches on how shoot counting can be done. Within canopy assessment, the emphasis is on methods to estimate canopy density. Other possible assessment areas are also discussed, such as canopy colour and measurement of canopy gaps and fruit exposure. An example of a vine assessment system is given.
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Sendashonga, Mireille. "Image quality assessment using frequency domain transforms." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99537.
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Measurement of image quality plays a central role in optimization and evaluation of imaging systems. The most straight-forward way to assess image quality is subjective evaluations by human observers, where the mean value of their scores is used as the quality measure. However, objective (quantitative) measures are needed because subjective evaluations are impractical and expensive. The aim of this thesis is to develop simple and low-complexity metrics for quality assessment of digital images.Traditionally, the most widely used quantitative measures are the mean squared error and measures that model the human visual system. The proposed method uses the Discrete Cosine Transform and the Discrete Wavelet Transform to divide images into four frequency bands and relates the visual quality of the distorted images to the weighted average of the mean squared error between original and distorted images within each band.
The performance of the metrics presented in this thesis is tested and validated on a large database of subjective quality ratings. Simulations show that the proposed metrics accurately predict visual quality and outperform current state-of-the-art methods with simple and easily implemented processing steps.
Extensions of the proposed image quality metrics are investigated. More particularly, this thesis explores image quality assessment when the reference image is only partially available (reduced reference settings), and presents a method for successfully quantifying the quality of distorted images in such settings.
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Belem, Brahima. "Non-invasive wound assessment by image analysis." Thesis, University of South Wales, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409107.
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Bailey, Samuel Gerard. "An image processing design and assessment methodology." Thesis, University College London (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249366.
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Tian, Shishun. "Image Quality Assessment of 3D Synthesized Views." Thesis, Rennes, INSA, 2019. http://www.theses.fr/2019ISAR0002/document.
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Depth-Image-Based Rendering (DIBR) est une technologie fondamentale dans plusieurs applications liées à la 3D, telles que la vidéo en mode point de vue libre (FVV), la réalité virtuelle (VR) et la réalité augmentée (AR). Cependant, l'évaluation de la qualité des vues synthétisées par DIBR a également posé de nouveaux problèmes, car ce processus induit de nouveaux types de distorsions, qui sont intrinsèquement différentes des distorsions provoquées par le codage vidéo. Ce travail est destiné à mieux évaluer la qualité des vues synthétisées par DIBR en multimédia immersif. Au chapitre 2, nous proposons deux métriques complètements sans référence (NR). Le principe de la première métrique NR NIQSV consiste à utiliser plusieurs opérations morphologiques d’ouverture et de fermeture pour détecter et mesurer les distorsions, telles que les régions floues et l’effritement. Dans la deuxième métrique NR NIQSV+, nous améliorons NIQSV en ajoutant un détecteur de “black hole” et une détection “stretching”.Au chapitre 3, nous proposons deux métriques de référence complète pour traiter les distorsions géométriques à l'aide d'un masque de désocclusion et d'une méthode de correspondance de blocs multi-résolution. Au chapitre 4, nous présentons une nouvelle base de données d'images synthétisée par DIBR avec ses scores subjectifs associés. Ce travail se concentre sur les distorsions uniquement induites par différentes méthodes de synthèse de DIBR qui déterminent la qualité d’expérience (QoE) de ces applications liées à DIBR. En outre, nous effectuons également une analyse de référence des mesures d'évaluation de la qualité objective de pointe pour les vues synthétisées par DIBR sur cette base de données. Le chapitre 5 conclut les contributions de cette thèse et donne quelques orientations pour les travaux futursDepth-Image-Based Rendering (DIBR) is a fundamental technology in several 3D-related applications, such as Free viewpoint video (FVV), Virtual Reality (VR) and Augmented Reality (AR). However, new challenges have also been brought in assessing the quality of DIBR-synthesized views since this process induces some new types of distortions, which are inherently different from the distortions caused by video coding. This work is dedicated to better evaluate the quality of DIBRsynthesized views in immersive multimedia. In chapter 2, we propose a completely No-reference (NR) metric. The principle of the first NR metrics NIQSV is to use a couple of opening and closing morphological operations to detect and measure the distortions, such as “blurry regions” and “crumbling”. In the second NR metric NIQSV+, we improve NIQSV by adding a “black hole” and a “stretching” detection. In chapter 3, we propose two Fullreference metrics to handle the geometric distortions by using a dis-occlusion mask and a multi-resolution block matching methods.In chapter 4, we present a new DIBR-synthesized image database with its associated subjective scores. This work focuses on the distortions only induced by different DIBR synthesis methods which determine the quality of experience (QoE) of these DIBR related applications. In addition, we also conduct a benchmark of the state-of-the-art objective quality assessment metrics for DIBR-synthesized views on this database. The chapter 5 concludes the contributions of this thesis and gives some directions of future work
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Havstad, Alexander. "Image quality assessment using artificial neural networks." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2005. https://ro.ecu.edu.au/theses/664.
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Hadley, Michael P. Wiest James A. "Integrated theater assessment profiling system /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FHadley.pdf.
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Thesis (M.S. in Information Technology Management and M.S. in Computer Science)--Naval Postgraduate School, September 2002.Thesis advisor(s): Alexander Bordetsky, Richard S. Coté. Michael P. Hadley received M.S. degrees in Information Technology Management and in Computer Science. James A. Wiest received a M.S. degree in Information Technology Management. Also available online.
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Narayanan, Sridar 1966. "Image analysis for the assessment of stereotactic radiosurgery." Thesis, McGill University, 1994. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=68229.
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This thesis describes an image-based system for analyzing dose distributions produced during treatment planning of stereotactic radiosurgery. A review of stereotaxy and radiosurgery, particularly for the treatment of arteriovenous malformations (AVMs), is presented. The stereotactic apparatus and linear accelerator-based irradiation techniques used at McGill University are described in some detail.The image-dose analysis software, running on a UNIX workstation, is presented, including software utilities for the evaluation of radiosurgery treatment plans based on magnetic resonance (MR) images. The importance of dose-volume histograms (DVHs) in the assessment of treatment plans is discussed, and the software developed to generate DVHs is described.
A technique for the experimental verification of calculated three-dimensional (3D) dose distributions is presented. This computerized approach is based on stereotactic principles and uses a plastic phantom and radiotherapy verification film as the dosimeter. The results indicate that, with some improvements, this is a viable technique for the experimental validation of stereotactic 3D treatment planning software.
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Jenadeleh, Mohsen [Verfasser]. "Blind Image and Video Quality Assessment / Mohsen Jenadeleh." Konstanz : Bibliothek der Universität Konstanz, 2018. http://d-nb.info/117308777X/34.
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Galkandage, Chathura. "Perception inspired stereoscopic image and video quality assessment." Thesis, University of Surrey, 2018. http://epubs.surrey.ac.uk/845426/.
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Recent developments in 3D media technology have brought to life numerous applications of interactive entertainment such as 3D cinema, 3DTV and gaming. Due to the data intensive nature of 3D visual content, Quality of Experience (QoE) has become a major driving factor to optimise the end-to-end content delivery process. However, to ensure the QoE, there is a need to develop more robust and accurate objective metrics for stereoscopic image and video quality assessment. Existing stereoscopic QoE metrics tend to lack in accuracy and robustness compared to its 2D counterparts as they are either extensions of 2D metrics or are based on simple perceptual models. However, measuring stereoscopic QoE requires more perceptually inspired metrics. This research introduces full-reference stereoscopic image and video quality metrics based on a Human Visual System (HVS) model incorporating important physiological findings on binocular vision. Firstly, a novel HVS model extending existing models in the literature is proposed to include the phenomena of binocular suppression and recurrent excitation towards stereoscopic image quality assessment. Secondly the research is extended to the temporal domain using temporal pooling of the HVS model outputs for individual frames and using a spatio-temporal model in the HVS model towards two distinct temporally inspired stereoscopic video quality metrics. Finally, motion sensitivity is introduced to the HVS model towards a perception inspired stereoscopic video quality metric. The proposed QoE metrics are trained, verified and tested using four publicly available stereoscopic image databases and two stereoscopic video datasets. They indicate an increase of average correlation index from 0.66 (baseline method) to 0.86 for the stereoscopic images and a maximum increase of average correlation index from 0.57 (baseline method) to 0.93 for stereoscopic videos. These results demonstrate the benefits of using a perceptually inspired approach in this research.
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Wang, Lei. "System Designs for Diabetic Foot Ulcer Image Assessment." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/67.
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For individuals with type 2 diabetes, diabetic foot ulcers represent a significant health issue and the wound care cost is quite high. Currently, clinicians and nurses mainly base their wound assessment on visual examination of wound size and the status of the wound tissue. This method is potentially inaccurate for wound assessment and requires extra clinical workload. In view of the prevalence of smartphones with high resolution digital camera, assessing wound healing by analyzing of real-time images using the significant computational power of today’s mobile devices is an attractive approach for managing foot ulcers. Alternatively, the smartphone may be used just for image capture and wireless transfer to a PC or laptop for image processing. To achieve accurate foot ulcer image assessment, we have developed and tested a novel automatic wound image analysis system which accomplishes the following conditions: 1) design of an easy-to-use image capture system which makes the image capture process comfortable for the patient and provides well-controlled image capture conditions; 2) synthesis of efficient and accurate algorithms for real-time wound boundary determination to measure the wound area size; 3) development of a quantitative method to assess the wound healing status based on a foot ulcer image sequence for a given patient and 4) design of a wound image assessment and management system that can be used both in the patient’s home and clinical environment in a tele-medicine fashion. In our work, the wound image is captured by the camera on the smartphone while the patient’s foot is held in place by an image capture box, which is specially design to aid patients in photographing ulcers occurring on the sole of their feet. The experimental results prove that our image capture system guarantees consistent illumination and a fixed distance between the foot and camera. These properties greatly reduce the complexity of the subsequent wound recognition and assessment. The most significant contribution of our work is the development of five different wound boundary determination approaches based on different computer vision algorithms. The first approach employs the level set algorithm to determine the wound boundary directly based on a manually set initial curve. The second and third approaches are the mean-shift segmentation based methods augmented by foot outline detection and analysis. These two approaches have been shown to be efficient to implement (especially on smartphones), prior-knowledge independent and able to provide reasonably accurate wound segmentation results given a set of well-tuned parameters. However, this method suffers from the lack of self-adaptivity due to the fact that it is not based on machine learning. Consequently, a two-stage Support Vector Machine (SVM) binary classifier based wound recognition approach is developed and implemented. This approach consists of three major steps 1) unsupervised super-pixel segmentation, 2) feature descriptor extraction for each super-pixel and 3) supervised classifier based wound boundary determination. The experimental results show that this approach provides promising performance (sensitivity: 73.3%, specificity: 95.6%) when dealing with foot ulcer images captured with our image capture box. In the third approach, we further relax the image capture constraints and generalize the application of our wound recognition system by applying the conditional random field (CRF) based model to solve the wound boundary determination. The key modules in this approach are the TextonBoost based potential learning at different scales and efficient CRF model inference to find the optimal labeling. Finally, the standard K-means clustering algorithm is applied to the determined wound area for color based wound tissue classification. To train the models used in the last two approaches, as well as to evaluate all three methods, we have collected about 100 wound images at the wound clinic in UMass Medical School by tracking 15 patients for a 2-year period, following an IRB approved protocol. The wound recognition results were compared with the ground truth generated by combining clinical labeling from three experienced clinicians. Specificity and sensitivity based measures indicate that the CRF based approach is the most reliable method despite its implementation complexity and computational demands. In addition, sample images of Moulage wound simulations are also used to increase the evaluation flexibility. The advantages and disadvantages of three approaches are described. Another important contribution of this work has been development of a healing score based mechanism for quantitative wound healing status assessment. The wound size and color composition measurements were converted to a score number ranging from 0-10, which indicates the healing trend based on comparisons of subsequent images to an initial foot ulcer image. By comparing the result of the healing score algorithm to the healing scores determined by experienced clinicians, we assess the clinical validity of our healing score algorithm. The level of agreement of our healing score with the three assessing clinicians was quantified by using the Kripendorff’s Alpha Coefficient (KAC). Finally, a collaborative wound image management system between the PC and smartphone was designed and successfully applied in the wound clinic for patients’ wound tracking purpose. This system is proven to be applicable in clinical environment and capable of providing interactive foot ulcer care in a telemedicine fashion.
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Marais, Izak van Zyl. "On-board image quality assessment for a satellite." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/1436.
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Thesis (PhD (Electronic Engineering))--University of Stellenbosch, 2009.The downloading of images is a bottleneck in the image acquisition chain for low earth orbit, remote sensing satellites. An on-board image quality assessment system could optimise use of available downlink time by prioritising images for download, based on their quality. An image quality assessment system based on measuring image degradations is proposed. Algorithms for estimating degradations are investigated. The degradation types considered are cloud cover, additive sensor noise and the defocus extent of the telescope. For cloud detection, the novel application of heteroscedastic discriminant analysis resulted in better performance than comparable dimension reducing transforms from remote sensing literature. A region growing method, which was previously used on-board a micro-satellite for cloud cover estimation, is critically evaluated and compared to commonly used thresholding. The thresholding method is recommended. A remote sensing noise estimation algorithm is compared to a noise estimation algorithm based on image pyramids. The image pyramid algorithm is recommended. It is adapted, which results in smaller errors. A novel angular spectral smoothing method for increasing the robustness of spectral based, direct defocus estimation is introduced. Three existing spectral based defocus estimation methods are compared with the angular smoothing method. An image quality assessment model is developed that models the mapping of the three estimated degradation levels to one quality score. A subjective image quality evaluation experiment is conducted, during which more than 18000 independent human judgements are collected. Two quality assessment models, based on neural networks and splines, are tted to this data. The spline model is recommended. The integrated system is evaluated and image quality predictions are shown to correlate well with human quality perception.
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Song, Weilian. "Image-Based Roadway Assessment Using Convolutional Neural Networks." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/78.
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Road crashes are one of the main causes of death in the United States. To reduce the number of accidents, roadway assessment programs take a proactive approach, collecting data and identifying high-risk roads before crashes occur. However, the cost of data acquisition and manual annotation has restricted the effect of these programs. In this thesis, we propose methods to automate the task of roadway safety assessment using deep learning. Specifically, we trained convolutional neural networks on publicly available roadway images to predict safety-related metrics: the star rating score and free-flow speed. Inference speeds for our methods are mere milliseconds, enabling large-scale roadway study at a fraction of the cost of manual approaches.
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Fisher, Jeffrey G. Wang Clare H. Czarney Jonathan R. "Organizational assessment of Shelter Outreach Plus /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Dec%5FFisher%5FMBA.pdf.
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Thesis (M.B.A.)--Naval Postgraduate School, December 2003."MBA professional report"--Cover. Thesis advisor(s): Cary Simon, Mary Malina. Includes bibliographical references (p. 39). Also available online.
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Zhang, Chen. "Blind Full Reference Quality Assessment of Poisson Image Denoising." University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1398875743.
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Ross, Victor B. "Using rapid environmental assessment to improve the hazard prediction and assessment capability for weapons of mass destruction." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Dec%5FRoss%5FMETOC.pdf.
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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, December 2003.Thesis advisor(s): Carlyle Wash, Neil Rowe. Includes bibliographical references (p. 47-48). Also available online.
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Radford, Brian W. "The Effect of Formative Assessments on Teaching and Learning." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3497.pdf.
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Trimble, Nicholas Robert. "Experimental assessment of aggregates." Thesis, Montana State University, 2007. http://etd.lib.montana.edu/etd/2007/trimble/TrimbleN0807.pdf.
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Li, Cui. "Image quality assessment using algorithmic and machine learning techniques." Thesis, Available from the University of Aberdeen Library and Historic Collections Digital Resources. Restricted: no access until June 2, 2014, 2009. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?application=DIGITOOL-3&owner=resourcediscovery&custom_att_2=simple_viewer&pid=26521.
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Thesis (Ph.D.)--Aberdeen University, 2009.With: An image quality metric based in corner, edge and symmetry maps / Li Cui, Alastair R. Allen. With: An image quality metric based on a colour appearance model / Li Cui and Alastair R. Allen. ACIVS / J. Blanc-Talon et al. eds. 2008 LNCS 5259, 696-707. Includes bibliographical references.
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Alpi, Davide. "Metodi di Deep Learning per Blind Image Quality Assessment." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23084/.
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Le tecniche di Deep Learning per Blind Image Quality Assessment consistono nella progettazione e sviluppo di reti neurali profonde per l'assegnazione di punteggi di qualità alle immagini. In questa tesi viene per prima cosa svolta un'indagine sugli approcci al problema presentati in letteratura. Viene presentato brevemente il funzionamento delle reti neurali convoluzionali e elencati dei modelli di successo, evidenziando per ciascuno le novità architetturali introdotte e i traguardi ottenuti in termini di prestazioni. Si procede poi con la progettazione di diversi modelli single-task e multi-task, utilizzando come architettura di partenza ResNet50 e EfficientNetB0. I task affrontati sono di regressione (predizione del punteggio di qualità) e di classificazione (riconoscimento delle categorie di scene raffigurate). Viene testato l'apprendimento automatico dei pesi assegnati ai due task nel caso multi-task. Vengono esposti e commentati i risultati di predizione del MOS anche in relazione alle classi di appartenenza.
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Zhu, Shu-Yu. "Perceptual wavelet coding and quality assessment for still image." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0020/MQ53450.pdf.
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Lee, Richard Leonard. "Image processing of radiographs in the assessment of osteoporosis." Thesis, University College London (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417850.
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Abdul, Manap Redzuan. "New learning frameworks for blind image quality assessment model." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/20945/.
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The focus of this thesis is on image quality assessment, specifically for problems of assessing the quality of an image blindly or without reference information. There are significant efforts over the last decade in developing objective blind models that can assess image quality as perceived by humans. Various models have been introduced, achieving highly competitive performances and high in correlation with subjective perceptual measures. However, there are still limitations on these models before they can be viable replacements to traditional image metrics over a wide range of image processing applications. This thesis addresses several limitations. The thesis first proposes a new framework to learn a blind image quality model with minimal training requirements, operates locally and has ability to identify distortion in the assessed image. To increase the model’s performance, the thesis then modifies the framework by considering an aspect of human vision tendency, which is often ignored by previous models. Finally, the thesis presents another framework that enable a model to simultaneously learn quality prediction for images affected by different distortion types.
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Chen, Zhili. "Mammographic image analysis : risk assessment and microcalcification classifcation aspects." Thesis, Aberystwyth University, 2013. http://hdl.handle.net/2160/14cb88d7-0d17-4741-ae61-25af5436c45d.
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Breast cancer is currently the most common cancer among women worldwide. Mammography has been the most reliable and e ective screening tool for the early detection of breast cancer. Recently, computer-aided diagnosis has become a major research topic in medical imaging and has been widely applied in clinical situations. This thesis investigates the employment of computer vision and image processing techniques for mammographic image analysis, which focuses on the aspects of mammographic risk assessment and microcalci cation classi cation. Many studies have indicated that mammographic density and mammographic parenchymal patterns are both strong predictive markers of breast cancer risk in mammographic images, which play an important role in estimating breast cancer risk. In this thesis, we present a variety of methods for estimating mammographic density and modelling mammographic parenchymal patterns. In order to build a complete framework for automated mammographic risk assessment, we rst develop a breast region segmentation method as a pre-processing step, which segments the breast region from mammograms to provide fundamental data for subsequent analysis. Subsequently, we propose two breast density segmentation methods to estimate mammographic density. The rst method is based on a modi ed fuzzy c-means algorithm which incorporates spatial information into the classic fuzzy c-means algorithm. The breast region is segmented into a number of sub-regions corresponding to di erent densities by clustering pixels with similar greylevel values. The second method exploits a topographic map to represent the overall pro le of breast tissue density within the breast. Dense tissue regions are segmented by detecting prominent/independent shapes based on a shape tree. For modelling mammographic parenchymal patterns, we present a method to model breast tissue appearance based on statistical analysis of local tissue appearance. Five di erent types of local features are investigated, covering the aspects of intensity, texture, and geometric structure. In addition, a multiscale blob based representation is proposed to model mammographic parenchymal patterns. Instead of statistically describing breast tissue appearance within the whole breast, we focus only on dense tissue with approximately blob-like structures. The validity of the proposed methods is evaluated using the MIAS and DDSM databases. A high agreement with expert radiologists is indicated according to the BIRADS density classi cation. The obtained classi cation accuracies are up to 79.44% and 81.23%, and increase to 93.15% and 91.70% for the low/high risk classi cation. For microcalci cation classi cation, a novel method is developed based on topological analysis. The connectivity between individual microcalci cations is analysed to classify microcalci cation clusters into malignant and benign. This method is evaluated using three datasets: MIAS, DDSM, and a full- eld digital dataset. High classi cation accuracies (up to 96%) and good ROC results (area under the ROC curve up to 0.97) are achieved.
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Sungkatavat, Sarinya. "Assessment of the U.S. travelers’ destination image of Thailand." Diss., Kansas State University, 2013. http://hdl.handle.net/2097/16911.
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Doctor of PhilosophyDepartment of Hospitality Management and Dietetics
Deborah Canter and Junehee Kwon
Thailand is one of the world’s most popular tourist destinations. However, it has not been successful in capturing and sustaining the U.S. travel market, one of the world’s top source markets by international tourism expenditure. The destination image (DI) is a key factor that affects destination selection, but there has been limited research exploring U.S. travelers’ DI of Thailand. Therefore, the purpose of this research was to measure U.S. travelers’ DI of Thailand and identify important attributes for U.S. travelers in terms of destination selection using mixed methods of qualitative and quantitative approaches. To explore American travelers’ DI of Thailand, personal interviews with 56 U.S. travelers were conducted at Suvarnabhumi Airport in Thailand. Maximum variation purposeful sampling was used to ensure diversity of the sample. One interviewer and a verifier ensured consistent and credible data collection and analyses. Data analyses included inductive, deductive and cross-case analyses. “Friendly People,” “Beaches and Islands,” and “Amazing” described Thailand's DI. First-time visitors had tourist attraction and activity-based images, while repeat visitors rated people and culture-related experience as top reasons for destination selection. Results from the qualitative study were used to identify common and unique attributes for the quantitative survey instrument. To access general perception of Thailand’s DI among U.S. travelers, an online survey was conducted with 522 international travelers including non-visitors (n=173), virtual-visitors (n=175) and visitors to Thailand (n=174). Descriptive statistics, ANOVA, and factor analyses were conducted. Of five factors that solidified the DI of Thailand, cultural attributes and local experiences represented Thailand’s DI the most. DIs were different among different types of visitors. Importance-Performance Analysis illustrated Thailand’s destination attributes and their importance for destination selection. For U.S. travelers, travel environment was the most important factor but had low performance. Thailand tourism organizations may use these findings for future marketing efforts toward the U.S. travel market.
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Bouraoui, Atidel. "Image quality assessment using an artificial neural network approach." Paris 13, 2012. http://scbd-sto.univ-paris13.fr/intranet/edgalilee_th_2012_lahoulou_ep_bouraoui.pdf.
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L’évaluation de la qualité d'image présente un intérêt substantiel pour les services ainsi que pour les systèmes de traitement d'images où le dernier maillon de la chaîne est l’observateur humain. En effet, la qualité d'image peut être mesurée de deux manières différentes. La première, appelée «évaluation subjective de la qualité", est l'approche évidente étant donnée la nature subjective de la qualité visuelle des médias. La seconde est appelée «évaluation objective de la qualité» qui permet de produire automatiquement des valeurs mesurant la qualité de l'image de manière quantitative. Il existe un large éventail de mesures d'évaluation objective de la qualité d'image pour lesquelles une taxonomie a été proposée au début de ce manuscrit. Le premier objectif de cette thèse est de fournir une évaluation statistique complète et approfondie des performances prédictives d'une large variété de mesures objectives de qualité avec référence complète sur un certain nombre de bases de données étiquetées avec des scores indiquant la qualité des images qui sont évaluées de manière subjective selon des protocoles strictes. Le second objectif consiste à définir les attributs de l'image qui sont les plus pertinents pour l’évaluation de sa qualité. Deux méthodes de sélection de caractéristiques ont été utilisées, à savoir la minimisation du risque structurel et l’approche basée sur le modèle connexionniste. Cela nous a permis de développer deux nouvelles métriques objectives de qualité d'image avec référence réduite où l’estimation de la qualité de l'image nécessite l'utilisation de seulement quelques uns des descripteurs de l’image de référence et celle de test. Le troisième objectif de ce travail de recherche est d'exploiter les techniques d'apprentissage supervisé, en particulier le modèle du perceptron multicouche, pour l’estimation automatique de la qualité de l'image. Le système apprend à partir des étiquettes de la qualité subjective issues des bases d’images utilisées et construit un modèle capable de généraliser après un certain temps d’entraînement. En d’autres termes, le modèle doit continuer à fournir une mesure objective toujours corresponde à l'avis de l'homme à toute image qui lui est présentée. L'objectif principal était d'optimiser la performance prédictive des mesures développées en fonction de la corrélation, la monotonicité et la précision. La fonction de coût par défaut basée sur l'erreur a été employée pour la première mesure développée (que nous avons appelé ECF) et une fonction de coût personnalisée basée sur la corrélation a été proposée pour concevoir la deuxième mesure (que nous avons appelé le CCF). L’étude comparative de ces deux nouvelles métriques à dix-huit autres algorithmes de qualité d'image avec référence complète sur trois bases de données de qualité d'image montre que les algorithmes d’ECF et CCF prennent en considération les non-linéarités du système visuel humain. L'ECF est plus précise que la majorité des mesures étudiées, tandis que la CCF améliore largement les résultats de toutes les métriques concurrentes en termes de corrélation et de monotonicitéImage quality assessment presents a substantial interest for image services that target human observers. Indeed, Image quality can be measured in two different ways. The first, called “subjective quality assessment”, is the obvious approach given the subjective nature of the visual data quality. The second one is called “objective quality assessment” that automatically allow to produce values that score image quality. There exists a large array of objective image quality assessment measures for which a taxonomic scheme has been proposed in the beginning of this manuscript. In fact, the first objective of this thesis is to provide a complete and thorough statistical predictive performance assessment of a variety of full‐reference objective quality measures over number of subjectively rated image quality databases. The second is to define the image attributes that are the most relevant to its quality evaluation. Two feature selection methods have been used including the structural risk minimization and the neural network based approaches. This allowed us to develop two new objective reduced‐reference image quality metrics where the image quality assessment requires the use of only a few features of the reference and the test images. The third objective of this research work is to exploit the supervised machine learning techniques, especially the multilayer perceptron based model, for automatic image quality appreciation. The system learns from the subjective quality scores and builds a model capable to further provide an objective measure that continues to match with the human opinion to any other image. The main target was to optimize the predictive performance of the developed measures according to correlation, monotonicity and accuracy. The default cost function based on error was employed for the first developed measure (that we called ECF) and a customized cost function based on correlation was proposed to design the second metric (that we called CCF). The comparative investigation to eighteen other full‐reference image quality algorithms over three image quality databases shows that both ECF and CCF take into consideration the nonlinearities of the human visual system. The ECF is more accurate than the majority of the metrics under study, while the CCF outperforms all its counterparts in terms of correlation and hence monotonicity
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Alaql, Omar abdulrahman. "GENERAL PURPOSE APPROACHES FOR NO-REFERENCE IMAGE QUALITY ASSESSMENT." Kent State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=kent1495821893652963.
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Pourebadi, Maryam. "A DEEP LEARNING APPROACH FOR BLIND IMAGE QUALITY ASSESSMENT." Kent State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=kent1499797575099571.
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Kang, Chen. "Image Aesthetic Quality Assessment Based on Deep Neural Networks." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG004.
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Avec le développement des dispositifs de capture et d'Internet, les gens accèdent à un nombre croissant d'images. L'évaluation de l'esthétique visuelle a des applications importantes dans plusieurs domaines, de la récupération d'image et de la recommandation à l'amélioration. L'évaluation de la qualité esthétique de l'image vise à déterminer la beauté d'une image pour les observateurs humains. De nombreux problèmes dans ce domaine ne sont pas bien étudiés, y compris la subjectivité de l'évaluation de la qualité esthétique, l'explication de l'esthétique et la collecte de données annotées par l'homme. La prédiction conventionnelle de la qualité esthétique des images vise à prédire le score moyen ou la classe esthétique d'une image. Cependant, la prédiction esthétique est intrinsèquement subjective, et des images avec des scores / classe esthétiques moyens similaires peuvent afficher des niveaux de consensus très différents par les évaluateurs humains. Des travaux récents ont traité de la subjectivité esthétique en prédisant la distribution des scores humains, mais la prédiction de la distribution n'est pas directement interprétable en termes de subjectivité et pourrait être sous-optimale par rapport à l'estimation directe des descripteurs de subjectivité calculés à partir des scores de vérité terrain. De plus, les étiquettes des ensembles de données existants sont souvent bruyantes, incomplètes ou ne permettent pas des tâches plus sophistiquées telles que comprendre pourquoi une image est belle ou non pour un observateur humain. Dans cette thèse, nous proposons tout d'abord plusieurs mesures de la subjectivité, allant de simples mesures statistiques telles que l'écart type des scores, aux descripteurs nouvellement proposés inspirés de la théorie de l'information. Nous évaluons les performances de prédiction de ces mesures lorsqu'elles sont calculées à partir de distributions de scores prédites et lorsqu'elles sont directement apprises à partir de données de vérité terrain. Nous constatons que cette dernière stratégie donne en général de meilleurs résultats. Nous utilisons également la subjectivité pour améliorer la prédiction des scores esthétiques, montrant que les mesures de subjectivité inspirées de la théorie de l'information fonctionnent mieux que les mesures statistiques. Ensuite, nous proposons un ensemble de données EVA (Explainable Visual Aesthetics), qui contient 4070 images avec au moins 30 votes par image. EVA a été collecté en utilisant une approche plus disciplinée inspirée des meilleures pratiques d'évaluation de la qualité. Il offre également des caractéristiques supplémentaires, telles que le degré de difficulté à évaluer le score esthétique, l'évaluation de 4 attributs esthétiques complémentaires, ainsi que l'importance relative de chaque attribut pour se forger une opinion esthétique. L'ensemble de données accessible au public devrait contribuer aux recherches futures sur la compréhension et la prédiction de l'esthétique de la qualité visuelle. De plus, nous avons étudié l'explicabilité de l'évaluation de la qualité esthétique de l'image. Une analyse statistique sur EVA démontre que les attributs collectés et l'importance relative peuvent être combinés linéairement pour expliquer efficacement les scores d'opinion moyenne esthétique globale. Nous avons trouvé que la subjectivité a une corrélation limitée avec la difficulté personnelle moyenne dans l'évaluation esthétique, et la région du sujet, le niveau photographique et l'âge affectent de manière significative l'évaluation esthétique de l'utilisateurWith the development of capture devices and the Internet, people access to an increasing amount of images. Assessing visual aesthetics has important applications in several domains, from image retrieval and recommendation to enhancement. Image aesthetic quality assessment aims at determining how beautiful an image looks to human observers. Many problems in this field are not studied well, including the subjectivity of aesthetic quality assessment, explanation of aesthetics and the human-annotated data collection. Conventional image aesthetic quality prediction aims at predicting the average score or aesthetic class of a picture. However, the aesthetic prediction is intrinsically subjective, and images with similar mean aesthetic scores/class might display very different levels of consensus by human raters. Recent work has dealt with aesthetic subjectivity by predicting the distribution of human scores, but predicting the distribution is not directly interpretable in terms of subjectivity, and might be sub-optimal compared to directly estimating subjectivity descriptors computed from ground-truth scores. Furthermore, labels in existing datasets are often noisy, incomplete or they do not allow more sophisticated tasks such as understanding why an image looks beautiful or not to a human observer. In this thesis, we first propose several measures of subjectivity, ranging from simple statistical measures such as the standard deviation of the scores, to newly proposed descriptors inspired by information theory. We evaluate the prediction performance of these measures when they are computed from predicted score distributions and when they are directly learned from ground-truth data. We find that the latter strategy provides in general better results. We also use the subjectivity to improve predicting aesthetic scores, showing that information theory inspired subjectivity measures perform better than statistical measures. Then, we propose an Explainable Visual Aesthetics (EVA) dataset, which contains 4070 images with at least 30 votes per image. EVA has been crowd-sourced using a more disciplined approach inspired by quality assessment best practices. It also offers additional features, such as the degree of difficulty in assessing the aesthetic score, rating for 4 complementary aesthetic attributes, as well as the relative importance of each attribute to form aesthetic opinions. The publicly available dataset is expected to contribute to future research on understanding and predicting visual quality aesthetics. Additionally, we studied the explainability of image aesthetic quality assessment. A statistical analysis on EVA demonstrates that the collected attributes and relative importance can be linearly combined to explain effectively the overall aesthetic mean opinion scores. We found subjectivity has a limited correlation to average personal difficulty in aesthetic assessment, and the subject's region, photographic level and age affect the user's aesthetic assessment significantly
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Hettiarachchi, Don Lahiru Nirmal Manikka. "An Accelerated General Purpose No-Reference Image Quality Assessment Metric and an Image Fusion Technique." University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1470048998.
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Young, Tyler Blaine. "Early-age strength assessment of cement-treated materials /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1779.pdf.
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Beavers, Aaron Evens. "Fish passage at UDOT culverts : prioritization & assessment /." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2580.pdf.
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Pelkola, Ryan James. "The European Union's headline goal : an operational assessment." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FPelkola.pdf.
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Thesis (M.A. in National Security Affairs)--Naval Postgraduate School, September 2002.Thesis advisor(s): David S. Yost, Tjarek Roessler. Includes bibliographical references. Also available online.
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Bullard, Steven G. "A qualitative assessment and analysis of stakeholder expectations." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03sep%5FBullard.pdf.
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Thesis (M.S. in Systems Engineering Management)--Naval Postgraduate School, September 2003.Thesis advisor(s): Walter E. Owen, Bryan Scurry. Includes bibliographical references (p. 87-90). Also available online.
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Teo, Tiat Leng. "Scenario selection and student assessment modules for CyberCIEGE." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Dec%5FTeo%5FT.pdf.
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Thesis (M.S. in Computer Science)--Naval Postgraduate School, December 2003.Thesis advisor(s): Cynthia Irvine, Michael Thompson. Includes bibliographical references (p. 103-105). Also available online.
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Baraheem, Samah Saeed. "Text to Image Synthesis via Mask Anchor Points and Aesthetic Assessment." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton158800567702413.
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Kim, Duhwan. "Multi-Scale Assessment of Geotextile-Geomembrane Interaction." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14019.
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Geotextile and geomembrane sheets are typically used as a composite system rather than as a stand-alone solution because of their complementary properties of permeability and stiffness. Previous researchers have focused on the large-scale interaction of fiber-texture interfaces while the micromechanical behavior of the internal geotextile structure has received limited attention. Characterizing the variation in the arrangement and distribution of filaments/voids is essential to understanding the micro-scale mechanisms of nonwoven fabrics interacting with counterface materials. This presentation summarizes the results from a study that examined the micromechanical mechanisms involved at needle-punched nonwoven geotextile-textured HDPE geomembrane interfaces and relates the results to the observed macro-scale response.
A large displacement direct interface shear device was developed and used in this study to reduce the system errors that often occur with conventional shear devices and to allow internal geotextile strains to occur during shear. Complimentary numerical modeling was undertaken to study interface response. An advanced image analysis technique was applied to allow the evolution of the filament microstructure under various boundary and load conditions to be quantified. The different phases within the geosynthetic interface zone were detected from images captured using high-resolution optical microscopy. The changes of geotextile inner structures were statistically quantified in terms of inter-filament distance changes as well as the local void ratio and inscribing void size distributions. The tensile response of single filaments was measured using a helium neon deflectometer and these measurements were used to evaluate the shear induced filament strain. The study provides insight into the combined role of geomembrane surface topography and geotextile filament structure on macro-scale geosynthetic interface response.
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Gao, Zhigang. "Image/video compression and quality assessment based on wavelet transform." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1187195053.
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Kinugasa, Taisuke. "Individualized assessment of conditioning for elite athletes /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18053.pdf.
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Granados, Daniel P. Kelly Kreg L. "Automated assessment of squadron enlisted manpower, training and readiness /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FGranados.pdf.
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Thesis (M.S. in Information Systems Technology)--Naval Postgraduate School, September 2002.Thesis advisor(s): John S. Osmundson, Dale M. Courtney. Includes bibliographical references (p. 148). Also available online.
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Nébouy, David. "Printing quality assessment by image processing and color prediction models." Thesis, Saint-Etienne, 2015. http://www.theses.fr/2015STET4018/document.
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L'impression, bien qu'étant une technique ancienne pour la coloration de surfaces, a connu un progrès considérable ces dernières années essentiellement grâce à la révolution du numérique. Les professionnels souhaitant remplir les exigences en termes de qualité du rendu visuel de leurs clients veulent donc savoir dans quelle mesure des observateurs humains sont sensibles à la dégradation d'une image. De telles questions concernant la qualité perçue d'une image reproduite peuvent être séparées en deux sujets différents: La qualité de l'impression, comme la capacité d'un système d'impression à reproduire fidèlement l'image d'origine, et la qualité d'une image imprimée, résultant à la fois de la qualité de reproduction, mais aussi de la qualité même de l'image numérique d'origine. Ce premier concept repose sur une analyse physique de la façon dont l'image d'origine est dégradée lors de son transfert sur un support, et nous proposons de la coupler avec une analyse sensorielle, visant à évaluer des attributs perceptuels et leur donner une valeur sur une certaine échelle, déterminée par des échantillons de référence classés par un ensemble d'observateurs. Le second concept inclut cette dégradation due à l’impression mais aussi la qualité perçu de l’image d’origine, qui ne fait pas parti de notre étude. Notre approche consiste d'abord à définir les différents indices de qualité, basés sur des critères mesurables en utilisant des outils d'évaluation basés sur des algorithmes "objectifs" de traitement d'image et des modèles optiques, sur une image imprimée-scannée. Thèse réalisée au Laboratoire Hubert CurienPrinting, though an old technique for surface coloration, considerably progressed these last decades especially thanks to the digital revolution. Professionals who want to meet the demands in terms of quality regarding the visual rendering of their clients thus want to know to which extent human observers are sensitive to the degradation of an image. Such questions regarding the perceived quality of a reproduced image can be split into two different topics: the printing quality as capacity of a printing system of accurately reproduce an original digital image, and the printed image quality which results from both the reproduction quality and the quality of the original image itself. The first concept relies on physical analysis of the way the original image is deteriorated when transferred onto the support, and we propose to couple it with a sensorial analysis, which aims at assessing perceptual attributes by giving them a value on a certain scale, determined with respect to reference samples classified by a set of observers. The second concept includes the degradation due to the printing plus the perceived quality of the original image, not in the scope of this work. In this report, we focus on the printing quality concept. Our approach first consists in the definition of several printing quality indices, based on measurable criteria using assessment tools based on “objective” image processing algorithms and optical models on a printed-then-scanned image. PhD work made in Hubert Curien Laboratory
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Bégin, Isabelle. "Camera-independent learning and image quality assessment for super-resolution." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102957.
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An increasing number of applications require high-resolution images in situations where the access to the sensor and the knowledge of its specifications are limited. In this thesis, the problem of blind super-resolution is addressed, here defined as the estimation of a high-resolution image from one or more low-resolution inputs, under the condition that the degradation model parameters are unknown. The assessment of super-resolved results, using objective measures of image quality, is also addressed.Learning-based methods have been successfully applied to the single frame super-resolution problem in the past. However, sensor characteristics such as the Point Spread Function (PSF) must often be known. In this thesis, a learning-based approach is adapted to work without the knowledge of the PSF thus making the framework camera-independent. However, the goal is not only to super-resolve an image under this limitation, but also to provide an estimation of the best PSF, consisting of a theoretical model with one unknown parameter.
In particular, two extensions of a method performing belief propagation on a Markov Random Field are presented. The first method finds the best PSF parameter by performing a search for the minimum mean distance between training examples and patches from the input image. In the second method, the best PSF parameter and the super-resolution result are found simultaneously by providing a range of possible PSF parameters from which the super-resolution algorithm will choose from. For both methods, a first estimate is obtained through blind deconvolution and an uncertainty is calculated in order to restrict the search.
Both camera-independent adaptations are compared and analyzed in various experiments, and a set of key parameters are varied to determine their effect on both the super-resolution and the PSF parameter recovery results. The use of quality measures is thus essential to quantify the improvements obtained from the algorithms. A set of measures is chosen that represents different aspects of image quality: the signal fidelity, the perceptual quality and the localization and scale of the edges.
Results indicate that both methods improve similarity to the ground truth and can in general refine the initial PSF parameter estimate towards the true value. Furthermore, the similarity measure results show that the chosen learning-based framework consistently improves a measure designed for perceptual quality.