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Shao, Lan, Liren Liu, and Guoqiang Li. "Solid state cellular two-layer fuzzy logic image processor." Journal of Optics 28, no. 4 (August 1997): 135–41. http://dx.doi.org/10.1088/0150-536x/28/4/001.
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Peguret, Muriel. "Le sentiment d’autoefficacité et les stratégies d’apprentissage chez les étudiants de français langue seconde venant d’immersion." Articles hors thème 40, no. 3 (March 13, 2015): 579–99. http://dx.doi.org/10.7202/1029075ar.
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Cette étude quantitative corrélationnelle explore la relation entre le sentiment d’autoefficacité des étudiants venant d’immersion, par rapport à l’apprentissage ou l’utilisation du français langue seconde en première année de français à l’université, et leur emploi de stratégies d’apprentissage. Nous avons découvert que le sentiment d’autoefficacité de nos participants est élevé. Les stratégies d’apprentissage sont employées assez fréquemment, mais ce sont les stratégies de compensation qui sont favorisées. Alors que le sentiment d’autoefficacité est lié à l’emploi de stratégies dans une majorité de contextes et de domaines d’expertise, il ne l’est généralement pas chez nos participants. Une explication possible pour ce résultat est que la pédagogie immersive véhicule une image incomplète des tâches d’apprentissage et d’utilisation de la langue qui a tendance à décourager l’emploi de stratégies d’apprentissage. Nous appelons à plus de recherches dans ce domaine, avec la visée générale d’aider les apprenants à développer un sentiment d’autoefficacité en français, qui serait à la fois optimiste et solide.
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Pelletier, Benoît. "Préface. Enracinement et identités." Francophonies d'Amérique, no. 26 (September 15, 2009): 17–24. http://dx.doi.org/10.7202/037972ar.
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Résumé Le thème de l’enracinement est particulièrement évocateur. Il fait allusion aux traces laissées par le passage du temps, à une prise solide sur le réel, à une présence profonde, soutenue et durable, à la longévité, à la fermeté et à la dignité. C’est le contraire de la superficialité, de la vacuité. Cette image convient parfaitement à la francophonie canadienne. Cette dernière n’est-elle pas fermement ancrée, fixée dans l’espace canadien ? Ne fait-elle pas partie depuis longtemps du paysage qui compose le Canada ? N’est-elle pas irriguée par l’histoire ? N’a-t-elle pas surgi d’un sol parfois aride pour se dresser d’un seul corps, croître et s’étendre comme l’arbre qui, de sa seule présence, semble défier son entourage ? Dans sa préface, l’auteur se sert de l’enracinement pour illustrer la constance et la ténacité qui caractérisent la présence française au Canada et dans le reste des Amériques. Renvoyant au passage aux nombreux défis que la mondialisation pose à la langue française et à l’identité francophone sur ce continent, il tisse un lien entre le passé et le présent et termine sur une note limpide, source de sa confiance en un avenir… en français !
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Wei, Jiaotong, Yan Han, and Ping Chen. "Narrow-Energy-Width CT Based on Multivoltage X-Ray Image Decomposition." International Journal of Biomedical Imaging 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/8126019.
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A polychromatic X-ray beam causes the grey of the reconstructed image to depend on its position within a solid and the material being imaged. This factor makes quantitative measurements via computed tomography (CT) imaging very difficult. To obtain a narrow-energy-width reconstructed image, we propose a model to decompose multivoltage X-ray images into many narrow-energy-width X-ray images by utilizing the low frequency characteristics of X-ray scattering. It needs no change of hardware in the typical CT system. Solving the decomposition model, narrow-energy-width projections are obtained and it is used to reconstruct the image. A cylinder composed of aluminum and silicon is used in a verification experiment. Some of the reconstructed images could be regarded as real narrow-energy-width reconstructed images, which demonstrates the effectiveness of the proposed method.
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Jahari, Ahmad Fadhil, Siti Rohaida Mohd Shafian, Hazlina Husin, Norzafirah Razali, and Sonny Irawan. "Quantification method of suspended solids in micromodel using image analysis." Journal of Petroleum Exploration and Production Technology 11, no. 5 (April 12, 2021): 2271–86. http://dx.doi.org/10.1007/s13202-021-01153-x.
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AbstractMicromodel can provide valuable information to improve understanding of pore-scale transport phenomenon and can also be utilized to simulate the transport process at pore scale. This research aims to propose settlement option for quantification of suspended solids in micromodel. The micromodel is used to mimic the formation damage which occurs in reservoir formation that could simultaneously affect enhanced oil recovery. This is done by utilizing visual image interpretation through image analysis on micromodel chip. Following the quantification of suspended solids, the micromodel was injected with brine that eventually forms agglomeration. Images are taken from NIS-Element AR microscope automatically in RGB color profile and then made into grayscale and finally into binary modes. Since the micromodel is simulated in 2D form structure, the quantification method complemented with image analysis is focusing on the quantified area, µm2 region of interest categorized into 3 main groups of area B05, M45 and T50, respectively. This research will explore on segmentation and thresholding processes of the visual data acquired from micromodel experiment. An image-based computational algorithm is programmed in MATLAB Image Processing Toolbox and ImageJ; hence, suspended solids in porous media could be quantified from the visual image executed in micromodel.
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You, Yun-Wen, Hsun-Yun Chang, Hua-Yang Liao, Wei-Lun Kao, Guo-Ji Yen, Chi-Jen Chang, Meng-Hung Tsai, and Jing-Jong Shyue. "Electron Tomography of HEK293T Cells Using Scanning Electron Microscope–Based Scanning Transmission Electron Microscopy." Microscopy and Microanalysis 18, no. 5 (October 2012): 1037–42. http://dx.doi.org/10.1017/s1431927612001158.
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AbstractBased on a scanning electron microscope operated at 30 kV with a homemade specimen holder and a multiangle solid-state detector behind the sample, low-kV scanning transmission electron microscopy (STEM) is presented with subsequent electron tomography for three-dimensional (3D) volume structure. Because of the low acceleration voltage, the stronger electron-atom scattering leads to a stronger contrast in the resulting image than standard TEM, especially for light elements. Furthermore, the low-kV STEM yields less radiation damage to the specimen, hence the structure can be preserved. In this work, two-dimensional STEM images of a 1-μm-thick cell section with projection angles between ±50° were collected, and the 3D volume structure was reconstructed using the simultaneous iterative reconstructive technique algorithm with the TomoJ plugin for ImageJ, which are both public domain software. Furthermore, the cross-sectional structure was obtained with the Volume Viewer plugin in ImageJ. Although the tilting angle is constrained and limits the resulting structural resolution, slicing the reconstructed volume generated the depth profile of the thick specimen with sufficient resolution to examine cellular uptake of Au nanoparticles, and the final position of these nanoparticles inside the cell was imaged.
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Sandford, James, Woodrow Barfield, and James Foley. "Empirical Studies of Interactive Computer Graphics: Perceptual and Cognitive Issues." Proceedings of the Human Factors Society Annual Meeting 31, no. 5 (September 1987): 519–23. http://dx.doi.org/10.1177/154193128703100508.
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Two experiments were performed to test the effects of varying computer graphics realism cues (wireframe vs. solid figures, flat vs. smooth shading for solid figures, and one or two light sources for solid figures) on the performance of a standard cognitive task (mental rotation) and on the subjective perceived realism of the computer-generated images. In the mental rotation experiment, mean reaction times were slower for wireframe than for smooth and flat shaded images and significant effects for figure complexity and angle of rotation were shown. In the second experiment, subjective ratings of image realism indicated that wireframe images were viewed as less realistic than solid model images and that number of light sources was more important in conveying image realism to users than was the type of shading.
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Do, A., A. M. Angulo, S. R. Nagel, G. N. Hall, D. K. Bradley, W. W. Hsing, L. A. Pickworth, N. Izumi, H. F. Robey, and Y. Zhou. "High spatial resolution and contrast radiography of hydrodynamic instabilities at the National Ignition Facility." Physics of Plasmas 29, no. 8 (August 2022): 080703. http://dx.doi.org/10.1063/5.0087214.
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We are developing techniques for studying the Rayleigh–Taylor (RT) and Richtmyer–Meshkov (RM) instabilities in a planar geometry at high-energy-densities at the National Ignition Facility (NIF). In particular, through the improvement of experimental imaging quality, we are progressing toward the study of the turbulent regime of the mixing regions in capsule implosion experiments for inertial confinement fusion, which requires few micrometers resolution. Using 60 NIF beams, a solid shock tube is driven launching a shock wave that crosses the interface between a dense and a light material pre-machined in the target to obtain sinusoidal ripples, which results in RM and RT instabilities that are imaged using the NIF Crystal Backlighter Imager. High-quality images were obtained with a mean resolution of 7 μm and improved contrast. While the obtained resolution does not allow the observation of the smallest scale of the “turbulent” energy spectrum, the generated image encompasses 63% of the total flow energy, a 50% improvement over previous studies, which is observed for the first time a roll-up feature in a high energy density-type RT experiment.
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Pham, Nam, Jong-Weon Lee, Goo-Rak Kwon, and Chun-Su Park. "Hybrid Image-Retrieval Method for Image-Splicing Validation." Symmetry 11, no. 1 (January 14, 2019): 83. http://dx.doi.org/10.3390/sym11010083.
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Recently, the task of validating the authenticity of images and the localization of tampered regions has been actively studied. In this paper, we go one step further by providing solid evidence for image manipulation. If a certain image is proved to be the spliced image, we try to retrieve the original authentic images that were used to generate the spliced image. Especially for the image retrieval of spliced images, we propose a hybrid image-retrieval method exploiting Zernike moment and Scale Invariant Feature Transform (SIFT) features. Due to the symmetry and antisymmetry properties of the Zernike moment, the scaling invariant property of SIFT and their common rotation invariant property, the proposed hybrid image-retrieval method is efficient in matching regions with different manipulation operations. Our simulation shows that the proposed method significantly increases the retrieval accuracy of the spliced images.
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Kim, H. G., W. Yoon, S. Rhee, and T. Kim. "AUTOMATIC METHOD FOR GENERATING 3D BUILDING MODELS WITH TEXTURE FROM UAV IMAGES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2022 (May 30, 2022): 375–82. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2022-375-2022.
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Abstract. In this paper, we propose a method for automatically generating 3D building models from UAV image based point cloud data and for mapping building texture from UAV images. The proposed method generates dense point clouds from UAV images and isolates points from building areas through a statistical analysis. It then generates solid models as 3D building models by point cloud analysis per building area. Texture for 3D building solid models are created by mapping model face to UAV images. In order to verify the proposed method, various UAV image sets and point clouds were tested. As results, the possibility of generating cluster-type solid building models based on UAV images was confirmed. It is expected that this method can contribute to the active usage of UAV images in 3D spatial information generation. In the future, we plan to conduct research on improving the accuracy of curved building shapes and texturing accuracy.
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Alliau, Damien, Sébastien Roux, and Léa Parelle. "Apports de la modélisation physique avec distorsions pour l'analyse des cours d'eau sableux : exemple de la Loire aval." La Houille Blanche, no. 2 (April 2020): 14–33. http://dx.doi.org/10.1051/lhb/2020013.
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Création de 700 épis dans le dernier siècle, extractions massives de sable réalisées pour les besoins de l'industrie … ces actions anthropiques ont eu des conséquences graves sur le fonctionnement de la Loire : enfoncement du lit, et mauvaise alimentation des annexes fluviales notamment. Dans le cadre du plan Loire IV (2014–2020) financé notamment par l'Union européenne, et dont la maîtrise d'ouvrage est en partie portée par Voies Navigables de France (VNF), la réalisation d'un ouvrage de correction sédimentaire a été proposée au voisinage de Nantes, avec l'objectif d'engendrer un remous solide régressif. Pour les études conception morpho-sédimentaires, la Compagnie Nationale du Rhône (CNR) a proposé une modélisation hybride ou composite, qui s'appuie notamment sur un modèle numérique 2D hydro-sédimentaire ainsi qu'un modèle physique de grande dimension : 1/100 pour l'échelle de longueur avec un facteur 2 de distorsion verticale, 1/33 pour l'échelle de densité des matériaux. Ce modèle à fond mobile a été conçu pour satisfaire à deux lois de similitudes dont la combinaison produit des distorsions géométriques et de densité des matériaux. La complexité d'un tel modèle impose un grand nombre de vérifications par l'expérience qu'il faut confronter aux approches théoriques de dimensionnement : du comportement granulaire aux macro-phénomènes (dunes). Un jeu de 341 dunes formées en laboratoire pendant plus de 50 essais a été analysé selon deux axes : le premier concerne la caractérisation géométrique (hauteur, longueur) grâce à la disponibilité de Modèles Numériques de Terrain capturés par un procédé de photogrammétrie submergée, et le second est focalisé sur la vitesse de migration des dunes par Large Scale Particle Image Velocimetry (LSPIV). L'ensemble des analyses réalisées a montré la très bonne adéquation entre les résultats de laboratoire et les phénomènes ligériens de transport de sable.
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Chen, Haitao. "Optimization of an Intelligent Sorting and Recycling System for Solid Waste Based on Image Recognition Technology." Advances in Mathematical Physics 2021 (December 3, 2021): 1–12. http://dx.doi.org/10.1155/2021/4094684.
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In this paper, the technique of image recognition algorithm is used to conduct an in-depth study and analysis of the intelligent classification and recycling system of solid waste and to optimize the design of its system. The network structure and detection principle of the YOLO target detection algorithm based on convolutional neural nets are analysed, images of construction solid waste are collected as a dataset, and the image dataset is expanded using data enhancement techniques, and the target objects in the dataset are labelled and used to train their own YOLO detection models. To facilitate testing the images and to design a YOLO algorithm-based construction solid waste target detection system. Using the detection system for construction solid waste recognition, the YOLO model can accurately detect the location, class, and confidential information of the target object in the image. Image recognition is a technique to recognize images by capturing real-life images through devices and performing feature extraction, and this technique has been widely used since its inception. The deep learning-based classification algorithm for recyclable solid waste studied in this paper can classify solid waste efficiently and accurately, solving the problem that people do not know how to classify solid waste in daily life. The convolutional layer, pooling layer, and fully connected layer in a convolutional neural network are responsible for feature extraction, reducing the number of parameters, integrating features into high-level features, and finally classifying them by SoftMax classifier in turn. However, the actual situation is intricate and often the result is not obtained as envisioned, and the use of migration learning can be a good way to improve the overfitting phenomenon. In this paper, the combination of lazy optimizer and lookahead can improve the generalization ability and fitting speed as well as greatly improve the accuracy and stability. The experimental results are tested, and it is found that the solid waste classification accuracy can be as high as 95% when the VGG19 model is selected and the optimizer is combined.
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Mustafa, Mustafa T., Khalid I. Hassoon, Hussain M. Hussain, and Modher H. Abd. "USING WATER INDICES (NDWI, MNDWI, NDMI, WRI AND AWEI) TO DETECT PHYSICAL AND CHEMICAL PARAMETERS BY APPLY REMOTE SENSING AND GIS TECHNIQUES." International Journal of Research -GRANTHAALAYAH 5, no. 10 (October 31, 2017): 117–28. http://dx.doi.org/10.29121/granthaalayah.v5.i10.2017.2289.
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This study was undertaken by analyzing data from satellite image (Landsat-8 OLI) and geographical information system (GIS) to find the relationship between water parameters and water indices of spectral images. The main purpose of this research was to develop a model for the physical and chemical parameters of Gharraf stream in Iraq. The water parameters used in this study included: acidity (PH), Total Dissolved Solids (T.D.S), Alkalinity(ALK), Electrical Conductivity (E.C), Calcium(Ca), Chloride (CL), Sodium (Na), Sulfate (SO4), Potassium (k), Total suspended solid (T.S.S), Total Hardness (TH).Where the samples were taken to seventeen stations with two seasons and at the same time took a satellite image on 4/FEB, 11 / MAY.GIS techniques were used in the beginning to project the coordinates of seventeen stations along the stream in Landsat-8 satellite image for extract data. Then, these data are treated in SPSS software for purpose finding correlation and regression equations. Positive strong correlations between the reflectance of the satellite image and the water parameters in 4/FEB and 11/ MAY with five stations, helped to build six regression models. These models could be used to predict these six water parameters (PH, E.c, CL, SO4, Na and K) at any point along the stream in Iraq from the satellite image directly.
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Treado, Patrick J., Ira W. Levin, and E. Neil Lewis. "High-Fidelity Raman Imaging Spectrometry: A Rapid Method Using an Acousto-Optic Tunable Filter." Applied Spectroscopy 46, no. 8 (August 1992): 1211–16. http://dx.doi.org/10.1366/0003702924123980.
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In this communication, we describe a technique for obtaining high-fidelity Raman images and Raman spectra. The instrumentation provides the ability to rapidly collect large-format images with the number of image pixels limited only by the number of detector elements in the silicon charge-coupled device (CCD). Wavelength selection is achieved with an acousto-optic tunable filter (AOTF), which maintains image fidelity while providing spectral selectivity. Under computer control the AOTF is capable of µs tuning speeds within the operating range of the filter (400–1900 nm). The AOTF is integrated with the CCD and holographic Raman filters to comprise an entirely solid-state Raman imager containing no moving parts. In operation, the AOTF is placed in front of the CCD and tuned over the desired spectral interval. The two-dimensional CCD detector is employed as a true imaging camera, providing a full multichannel advantage over competitive Raman imaging techniques. Images and spectra are presented of a mixture of dipalmitoylphosphatidylcholine (DPPC) and L-asparagine, which serves as a model system for the study of both lipid/peptide and lipid/protein interactions in intact biological materials. The Raman images are collected in only several seconds and indicate the efficacy of this rapid technique for discriminating between multiple components in complex matrices. Additionally, high-quality Raman spectra of the spatially resolved microscopic regions are easily obtained.
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Kondo, Masaki. "Unfolding the In-between Image: The Emergence of an Incipient Image at the Intersection of Still and Moving Images." Contemporaneity: Historical Presence in Visual Culture 3 (June 5, 2014): 50–61. http://dx.doi.org/10.5195/contemp.2014.80.
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As digital technology has transformed various aspects of our screen culture over the past few decades, we have been witnessing a disappearing boundary between photographic still images and cinematic moving images. An emerging in-between image has become increasingly prominent in this new image culture, which attempts to negotiate the grey area between stillness and movement. This in-between image, manifest in a variety of formats and media, points to an increasingly solid middle ground between the traditional divisions of still and moving images. This paper builds a conceptual framework for analysing this new type of image and explores both the roots of this emergent category before focusing on its contemporary trajectory as exemplified by the work of Adad Hannah, Hiroshi Sugimoto, Jeff Wall, and James Nares.
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Arai, Shigeo, Susumu Tsukimoto, Shunsuke Muto, and Hiroyasu Saka. "Direct Observation of the Atomic Structure in a Solid–Liquid Interface." Microscopy and Microanalysis 6, no. 4 (July 2000): 358–61. http://dx.doi.org/10.1017/s1431927602000612.
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AbstractAn experimental high-resolution image of a solid–liquid interface of solid Si and liquid Al–Si alloy has been compared with theoretical images obtained by computer simulation. It has been concluded that the solid–liquid interface has a transition layer, the structure of which is compatible with the 1 × 1 Si-{111} surface.
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Arai, Shigeo, Susumu Tsukimoto, Shunsuke Muto, and Hiroyasu Saka. "Direct Observation of the Atomic Structure in a Solid–Liquid Interface." Microscopy and Microanalysis 6, no. 4 (July 2000): 358–61. http://dx.doi.org/10.1007/s100050010030.
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Abstract An experimental high-resolution image of a solid–liquid interface of solid Si and liquid Al–Si alloy has been compared with theoretical images obtained by computer simulation. It has been concluded that the solid–liquid interface has a transition layer, the structure of which is compatible with the 1 × 1 Si-{111} surface.
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White, Amanda J., and Denton S. Ebel. "Imaging Samples in Silica Aerogel Using an Experimental Point Spread Function." Microscopy and Microanalysis 21, no. 1 (December 17, 2014): 172–78. http://dx.doi.org/10.1017/s1431927614013610.
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AbstractLight microscopy is a powerful tool that allows for many types of samples to be examined in a rapid, easy, and nondestructive manner. Subsequent image analysis, however, is compromised by distortion of signal by instrument optics. Deconvolution of images prior to analysis allows for the recovery of lost information by procedures that utilize either a theoretically or experimentally calculated point spread function (PSF). Using a laser scanning confocal microscope (LSCM), we have imaged whole impact tracks of comet particles captured in silica aerogel, a low density, porous SiO2 solid, by the NASA Stardust mission. In order to understand the dynamical interactions between the particles and the aerogel, precise grain location and track volume measurement are required. We report a method for measuring an experimental PSF suitable for three-dimensional deconvolution of imaged particles in aerogel. Using fluorescent beads manufactured into Stardust flight-grade aerogel, we have applied a deconvolution technique standard in the biological sciences to confocal images of whole Stardust tracks. The incorporation of an experimentally measured PSF allows for better quantitative measurements of the size and location of single grains in aerogel and more accurate measurements of track morphology.
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Li, Tongtong, Qiang Lin, Yanru Guo, Shaofang Zhao, Xianwu Zeng, Zhengxing Man, Yongchun Cao, and Yonghua Hu. "Automated detection of skeletal metastasis of lung cancer with bone scans using convolutional nuclear network." Physics in Medicine & Biology 67, no. 1 (January 7, 2022): 015004. http://dx.doi.org/10.1088/1361-6560/ac4565.
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Abstract A bone scan is widely used for surveying bone metastases caused by various solid tumors. Scintigraphic images are characterized by inferior spatial resolution, bringing a significant challenge to manual analysis of images by nuclear medicine physicians. We present in this work a new framework for automatically classifying scintigraphic images collected from patients clinically diagnosed with lung cancer. The framework consists of data preparation and image classification. In the data preparation stage, data augmentation is used to enlarge the dataset, followed by image fusion and thoracic region extraction. In the image classification stage, we use a self-defined convolutional neural network consisting of feature extraction, feature aggregation, and feature classification sub-networks. The developed multi-class classification network can not only predict whether a bone scan image contains bone metastasis but also tell which subcategory of lung cancer that a bone metastasis metastasized from is present in the image. Experimental evaluations on a set of clinical bone scan images have shown that the proposed multi-class classification network is workable for automated classification of metastatic images, with achieving average scores of 0.7392, 0.7592, 0.7242, and 0.7292 for accuracy, precision, recall, and F-1 score, respectively.
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Hokka, Jenni, and Matti Nelimarkka. "Affective economy of national-populist images: Investigating national and transnational online networks through visual big data." New Media & Society 22, no. 5 (August 21, 2019): 770–92. http://dx.doi.org/10.1177/1461444819868686.
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In our article, we investigate the affective economy of national-populist image circulation on Facebook. This is highly relevant, since social media has been an essential area for the spread of national-populist ideology. In our research, we analyse image circulation as affective practice, combining qualitative and quantitative methods. We use computational data analysis methods to examine visual big data: image fingerprints and reverse image search engines to track down the routes of thousands of circulated images as well as make discourse-historical analysis on the images that have gained most attention among supporters. Our research demonstrates that these existing tools allow social science research to make theory-solid approaches to understand the role of image circulation in creating and sustaining national and transnational networks on social media, and show how national-populist thinking is spread through images that catalyse and mobilise affects – fear, anger and resentment – thus creating an effective affective economy.
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Liu, X. K., Q. Yu, X. H. Pan, Z. H. Yu, and X. X. Lu. "Image contrast enhancement algorithm for X-ray observation of space materials in situ." Journal of Instrumentation 17, no. 06 (June 1, 2022): P06010. http://dx.doi.org/10.1088/1748-0221/17/06/p06010.
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Abstract Chinese Space Station has planned a high-temperature material science experiment rack, equipped with an X-ray projection imaging module, to support the development of material experiments and research in space. BiFeO3 has been selected as the first batch of experimental materials for Chinese Space Station. The melting and solidification process of BiFeO3, an opaque, high-temperature material, is observed by X-ray observation module in situ. X-ray is the dominant way to observe opaque materials due to its penetrability. In-situ observation of materials is the top priority of this study, so we have strict requirements on image quality, and high-quality images can better analyze the properties and properties of materials. Limited by narrow size and high temperature conditions, the X-ray images collected have low contrast, serious noise pollution, and poor imaging quality. To enhance the contrast and improve the edge details of such images, a grayscale weighted histogram equalization combined with high-frequency enhancement (GWHE-HFE) algorithm is proposed. First, we add a mask to the input image to obtain the region of interest (ROI), and then filter out the low-frequency components of the image by Gaussian high-pass filter to preserve high-frequency details. Second, the image obtained in the previous and the X-ray image of ROI are respectively multiplied by a coefficient and added to obtain the edge-emphasized X-ray image. And then, we use grayscale weighted histogram equalization (GWHE) to process the image obtained in the second step to obtain the contrast enhanced X-ray image. The enhanced image shows the crystal grains and the thin bands where the solid and the melt intersect, and it is helpful to accurately locate the solid solution interface. Experiments on X-ray images of BiFeO3 growth demonstrate that this combined method outperforms existing ones both qualitatively and quantitatively, providing an in-depth and effective analysis method for in high-temperature material-science experiments.
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Yao, Xiu Hong, and Wen Xing Bao. "High Resolution RS Image Industrial Solid Wastes Extraction Based on SVM." Applied Mechanics and Materials 543-547 (March 2014): 2318–22. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2318.
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In order to accurately extract various types of industrial solid wastes from high resolution RS images, a industrial solid wastes feature fast extraction algorithm was proposed based on SVM. The reasonable image pretreatment was conducted by anisotropic diffusion filtering firstly. It is because that high resolution RS image contains abundant information and industrial solid wastes heap was very complex, we proposed the classification algorithm based on 1-v-1 which could extract multi-class industrial solid wastes fast and accurately at once. The new algorithm improved both efficiency and accuracy of industrial solid wastes recognition. The experimental results show that the industrial solid wastes feature recognition of SVM has better advantages than conventional methods. The new algorithm can recognize not only shape features of industrial solid wastes heap but also its material and type and it is constructed to recognize multi-class industrial solid wastes with higher operation efficiency.
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Zhao, Jiangsan, Dmitry Kechasov, Boris Rewald, Gernot Bodner, Michel Verheul, Nicholas Clarke, and Jihong Liu Clarke. "Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters." Remote Sensing 12, no. 19 (October 7, 2020): 3258. http://dx.doi.org/10.3390/rs12193258.
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Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond.
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Chen, Ning, Shu Sen Yang, and Hui Ze Xu. "The Application of Image Entropy in the Recognition of Free Fluid Spread Motion." Applied Mechanics and Materials 665 (October 2014): 685–90. http://dx.doi.org/10.4028/www.scientific.net/amm.665.685.
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In this paper, we introduce the image entropy value into the image pattern recognition of movement, to distinguish the differences between solid movement and diffusion movement. Through the theoretical analysis of the fluid free diffusion movement, the characteristics of the movement were summed up, and the effect of the movement on images was studied. Through simulation and calculation of different movement, their effects on image entropy’s variation were summarized. Finally, through experiments, the image entropy’s variation in practical application was explored, and the results were analyzed. The result turns out to be positive.
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Chen, Hong Qian, Zhao Shen Qing, Da Zhou Zhu, Zhen Hua Tu, and Chao Ying Meng. "Honey Quality Predictions Based on Vis-NIR Laser Diffuse Reflectance Image." Applied Mechanics and Materials 195-196 (August 2012): 1321–26. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.1321.
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Based on machine version and laser image analysis, the lasers with wavelength of 980 nm, 938 nm, and 780 nm were used to detect the water and soluble solids content (SSC) of honey samples. In the course of the experiment, an image acquisition platform was designed to collect the images formed on the surface of each honey sample, and the image processing software was used to extract the images feature parameters. The results showed that the models based on mean value of intensities had the r value of 0.4268 for water and 0.3882 for SSC, the model based on the frequency of intensities had the r value of 0.6465 for water and 0.6226 for SSC, the model of the frequency of intensities had the r value of 0.7061 for water and 0.7083 for SSC after correction, which demonstrated that the laser image technology had potential to detect the quality of honey.
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Ninsalam, Y., R. Qin, and J. Rekittke. "APPLICATION FOR 3D SCENE UNDERSTANDING IN DETECTING DISCHARGE OF DOMESTICWASTE ALONG COMPLEX URBAN RIVERS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 663–67. http://dx.doi.org/10.5194/isprs-archives-xli-b3-663-2016.
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In our study we use 3D scene understanding to detect the discharge of domestic solid waste along an urban river. Solid waste found along the Ciliwung River in the neighbourhoods of Bukit Duri and Kampung Melayu may be attributed to households. This is in part due to inadequate municipal waste infrastructure and services which has caused those living along the river to rely upon it for waste disposal. However, there has been little research to understand the prevalence of household waste along the river. Our aim is to develop a methodology that deploys a low cost sensor to identify point source discharge of solid waste using image classification methods. To demonstrate this we describe the following five-step method: 1) a strip of GoPro images are captured photogrammetrically and processed for dense point cloud generation; 2) depth for each image is generated through a backward projection of the point clouds; 3) a supervised image classification method based on Random Forest classifier is applied on the view dependent red, green, blue and depth (RGB-D) data; 4) point discharge locations of solid waste can then be mapped by projecting the classified images to the 3D point clouds; 5) then the landscape elements are classified into five types, such as vegetation, human settlement, soil, water and solid waste. While this work is still ongoing, the initial results have demonstrated that it is possible to perform quantitative studies that may help reveal and estimate the amount of waste present along the river bank.
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Ninsalam, Y., R. Qin, and J. Rekittke. "APPLICATION FOR 3D SCENE UNDERSTANDING IN DETECTING DISCHARGE OF DOMESTICWASTE ALONG COMPLEX URBAN RIVERS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 663–67. http://dx.doi.org/10.5194/isprsarchives-xli-b3-663-2016.
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In our study we use 3D scene understanding to detect the discharge of domestic solid waste along an urban river. Solid waste found along the Ciliwung River in the neighbourhoods of Bukit Duri and Kampung Melayu may be attributed to households. This is in part due to inadequate municipal waste infrastructure and services which has caused those living along the river to rely upon it for waste disposal. However, there has been little research to understand the prevalence of household waste along the river. Our aim is to develop a methodology that deploys a low cost sensor to identify point source discharge of solid waste using image classification methods. To demonstrate this we describe the following five-step method: 1) a strip of GoPro images are captured photogrammetrically and processed for dense point cloud generation; 2) depth for each image is generated through a backward projection of the point clouds; 3) a supervised image classification method based on Random Forest classifier is applied on the view dependent red, green, blue and depth (RGB-D) data; 4) point discharge locations of solid waste can then be mapped by projecting the classified images to the 3D point clouds; 5) then the landscape elements are classified into five types, such as vegetation, human settlement, soil, water and solid waste. While this work is still ongoing, the initial results have demonstrated that it is possible to perform quantitative studies that may help reveal and estimate the amount of waste present along the river bank.
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Zhuang, Zilong, Ying Liu, Yutu Yang, Yinxi Shen, and Binli Gou. "Color Regression and Sorting System of Solid Wood Floor." Forests 13, no. 9 (September 10, 2022): 1454. http://dx.doi.org/10.3390/f13091454.
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Solid wood floors are very common in interior decoration, and their color is an important indicator of product quality, selected in order to achieve the overall aesthetic needed to ensure color consistency. In order to realize the sorting of solid wood floors based on color depth, so that the colors of solid wood floors could be freely graded, one image acquisition system was built to collect 108 solid wood floor images and a set of fast sorting methods for solid wood floor color depth was developed. Among these, 10 solid wood floor images were used as the test set and therefore not sorted, and 98 solid wood floor images were sorted by color depth. Among these, 80 original images were expanded 13 times to 1040, for use as a training set, and 18 were used as a validation set. The color characteristics of solid wood floors in RGB, HSV and Lab color space were extracted, and LightGBM was used to realize the color depth sorting of the solid wood floors. At the same time, two deep learning algorithms, the Vision Transformer as well as Densenet121, improved by means of an adaptive pooling layer, were used to realize the color depth sorting of solid wood floor images of different sizes. The final ranking results showed that the color ranking method using LightGBM to regress the color features exhibited the most harmonious final results.
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Setiabudi, Agus, Asep Wahyudin, Galuh Yuliani, and Mauro Mocerino. "Microscopic Observation of Solid-Liquid Reaction: A Novel Laboratory Approach to Teaching Rate of Reaction." Indonesian Journal of Chemistry 17, no. 1 (April 1, 2017): 119. http://dx.doi.org/10.22146/ijc.23642.
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The importance of observation in science and science education has triggered this laboratory development study that investigated the value of an observation kit as a new approach to teaching rate of reaction in general chemistry class. The kit consists of a digital microscope, a “chemical reactor”, and a tailor-made computer application and was used to video-record a solid-liquid reaction and to produce a series of two dimensional solid images that indicate the extent of reaction. The two dimensional image areas were calculated by the computer application and using the assumption that the image area was directly proportional to the mass of the solid, a plot of solid mass versus time could be obtained. These steps have been tested in several solid-liquid reaction systems, with the reaction of solid magnesium oxide with nitric acid solution resulting in the best images which were transferable to rate of reaction data, i.e. a plot of solid MgO mass as a function of time. The plot can be used to explain rate of reaction concepts including average, instantaneous, and initial rate. Furthermore, the effect of concentration on reaction rate could also be explained. This study showed that the observation kit and the generated data set have the advantage of allowing students to clearly and repeatedly visualise a solid-liquid reaction and relate this with the concept of rates of reactions. The observation kit also allows teachers and students to extend its application into inquiry based experiments.
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Wells, Oliver C. "Optimizing the collector solid angle for the low-loss electron image in the Scanning Electron Microscope." Proceedings, annual meeting, Electron Microscopy Society of America 45 (August 1987): 548–49. http://dx.doi.org/10.1017/s042482010012730x.
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The low-loss electron (LLE) image in the scanning electron microscope (SEM) is formed by collecting backscattered electrons (BSE) that have lost less than a specified energy. Compared to the secondary electron (SE) image, these images are less affected by specimen charging and show the surface topography clearly when examining uncoated photoresist. However, LLE images sometimes contain dark shadows caused by the limited solid angle of the LLE detector. Here, we describe a way to position the sample (with a given LLE detector) so as to reduce these shadows as far as possible.The SEM was a Cambridge S-250 Mk. III with a tungsten filament. An experimental LLE detector was added. The SE image was obtained using the SE detector ordinarily present in the SEM.The LLE detector is shown in Fig. 1. The specimen is mounted close to the lens in the SEM with a glancing angle of incidence of 30°.
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Yin, Jian Jun, Jia Qing Lin, S. Mittal Gauri, and Shuang Li. "Reverse Design and Application of Irregular Planar Part Based on Image Processing." Applied Mechanics and Materials 229-231 (November 2012): 1706–9. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1706.
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By using a computer vision detection system to obtain high resolution images of a machine part, a kind of reverse design method of solid modeling of irregular planar part with aided implementation of computer vision was proposed in this paper, which integrates image processing function of Matlab software with solid modeling function of computer aided design (CAD) software. The method used a calibrated digital camera to get the image of the tested part, a three-dimensional entity vector model may be built up after image inversion, edge detection, vectorization process of binary image and size matching were operated sequentially. The results of image reverse design showed that it is an easy and convenient way to reverse irregular planar parts based on image processing. One of its remarkable advantages is the saving of design period and the reduction of design cost. Its measurement error can be controlled within 0.1 mm, and can meet general precision requirement of application occasions. Reversed parts may provide a model basis for further analysis on mechanism assembling and motion simulation.
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Pan, M., and P. A. Crozier. "Low-dose high-resolution EM of zeolite materials with slow A scan CCD camera." Proceedings, annual meeting, Electron Microscopy Society of America 50, no. 1 (August 1992): 302–3. http://dx.doi.org/10.1017/s0424820100121910.
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Zeolite materials are widely used as an important type of catalyst in oil industry. Their catalytic properties and performance are closely related to their unique structures. Use of high resolution electron microscopy (HREM) to characterize structures of zeolite materials has been limited mainly due to the great sensitivity of the framework structures to electron beam irradiation used in the observation. With the recent development in solid state electronics, a new type of image recording device, known as a charge-coupled-device (CCD), has been made possible. Among many of its superior properties, it has been found that the very low noise level present in a slow scan CCD camera offers the possibility of recording high resolution structure images of zeolite materials with very low electron beam dose. The digital storage of CCD images allows on-line image processing to be performed at the microscope, thus making the recording of low dose electron microscope images more efficient. Image processing was also found to be essential in extracting high resolution structure information contained in the noisy low dose HREM CCD images.
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Perdukova, Daniela, Mišel Batmend, and Pavol Fedor. "Automated Headstone Photo Engraving." Applied Mechanics and Materials 816 (November 2015): 313–20. http://dx.doi.org/10.4028/www.scientific.net/amm.816.313.
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Nowadays, machine engraving of photos into solid materials such as marble or granite is becoming very popular. Relatively cheap CNC machines are available. The problem is that high quality photos are essential to obtain good results. The first part of the paper describes a model of a CNC machine used for engraving and puts down the principles of image processing applied to poor quality photos in order to get the best results, as well as the fundamental image processing methods necessary for achieving satisfactory results when using an electromagnetic diamond percussion tool for engraving. The second part of the paper describes a very simple method of data coding and the algorithm of engraving tool movement for image engraving process by means of a control system based on ATmega16 microcontroller. The quality of the engraved images is comparable, or even better, than that of manually engraved images or images engraved by other competitive CNC machines.
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Hobbs, Linn W. "Beam-Induced Indeterminacies In The Ivem." Proceedings, annual meeting, Electron Microscopy Society of America 48, no. 1 (August 12, 1990): 484–85. http://dx.doi.org/10.1017/s0424820100181178.
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Intermediate-voltage electron microscopes accelerate electrons to energies U between 200 keV and 600 keV to take advantage of a U-3/8 improvement in resolving power which enables sub-0.2nm atomic structure projections to be routinely imaged. This exciting prospect is, however, purchased at the price of uncertainties in the integrity of the images obtained, because the investigating probe unavoidably perturbs the structure which is being imaged. Such perturbations have been relegated historically to the anecdotal realm of “radiation effects” but are really a perverse manifestation of the Uncertainty Principle. That electron microscopists are able to resolve the structure of solids at all is a tribute to the strength and mutliplicity of atomic bonds in solids and the large “recoiless fraction” (in the parlance of nuclear spectroscopies like the Mossbauer effect) for which the whole solid, and not an individual atom, recoils from the impact of the investigating probe.Atomic structure is sensitive to the impact of incident IVEM electrons because they lose energy (at a rate of order 1 GeV/m) in traversing a solid, a small portion of which is available as kinetic energy to restructure atomic positions. About 98% of the energy loss goes into electronic excitations which can in certain materials (notably organic materials, halides, silicates and almost anything explosive) destabilize atomic positions within the specimen interior with unnervingly high efficiency by radiolytic processes. Analogous radiolytic loss or restructuring of surface atoms, whose bonding constraints are fewer than for atoms in the interior, has been coined “desorption induced by electronic transitions” (DIET). Of the remaining 2% of the energy loss, which is transferred instead to atomic nuclei, most goes into the generation of heat (the eventual fate of most non-radiative electronic transitions as well), but about 1 part in 105 (or 1 part in 107 overall) is available to generate atomic displacements ballistically, by direct knockon: at the surface for almost all solids examined by IVEM (leading to sputtering) and in the interior for many medium atomic-weight solids.
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Cortina-Januchs, M. G., J. Quintanilla-Dominguez, A. Vega-Corona, A. M. Tarquis, and D. Andina. "Detection of pore space in CT soil images using artificial neural networks." Biogeosciences Discussions 7, no. 4 (August 16, 2010): 6173–205. http://dx.doi.org/10.5194/bgd-7-6173-2010.
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Abstract. Computed Tomography (CT) images provide a non-invasive alternative for observing soil structures, particularly pore space. Pore space in soil data indicates empty or free space in the sense that no material is present there except fluids such as air, water, and gas. Fluid transport depends on where pore spaces are located in the soil, and for this reason, it is important to identify pore zones. The low contrast between soil and pore space in CT images presents a problem with respect to pore quantification. In this paper, we present a methodology that integrates image processing, clustering techniques and artificial neural networks, in order to classify pore space in soil images. Image processing was used for the feature extraction of images. Three clustering algorithms were implemented (K-means, fuzzy C-means, and self organizing maps) to segment images. The objective of clustering process is to find pixel groups of a similar grey level intensity and to organise them into more or less homogeneous groups. The segmented images are used for test a classifier. An artificial neural network is characterised by a great degree of modularity and flexibility, and it is very efficient for large-scale and generic pattern recognition applications. For these reasons, an artificial neural network was used to classify soil images into two classes (pore space and solid soil). Our methodology shows an alternative way to detect solid soil and pore space in CT images. The percentages of correct classifications of pore space of the total number of classifications among the tested images were 97.01%, 96.47% and 96.12%.
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Cortina-Januchs, M. G., J. Quintanilla-Dominguez, A. Vega-Corona, A. M. Tarquis, and D. Andina. "Detection of pore space in CT soil images using artificial neural networks." Biogeosciences 8, no. 2 (February 9, 2011): 279–88. http://dx.doi.org/10.5194/bg-8-279-2011.
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Abstract. Computed Tomography (CT) images provide a non-invasive alternative for observing soil structures, particularly pore space. Pore space in soil data indicates empty or free space in the sense that no material is present there except fluids such as air, water, and gas. Fluid transport depends on where pore spaces are located in the soil, and for this reason, it is important to identify pore zones. The low contrast between soil and pore space in CT images presents a problem with respect to pore quantification. In this paper, we present a methodology that integrates image processing, clustering techniques and artificial neural networks, in order to classify pore space in soil images. Image processing was used for the feature extraction of images. Three clustering algorithms were implemented (K-means, Fuzzy C-means, and Self Organising Maps) to segment images. The objective of clustering process is to find pixel groups of a similar grey level intensity and to organise them into more or less homogeneous groups. The segmented images are used for test a classifier. An Artificial Neural Network is characterised by a great degree of modularity and flexibility, and it is very efficient for large-scale and generic pattern recognition applications. For these reasons, an Artificial Neural Network was used to classify soil images into two classes (pore space and solid soil). Our methodology shows an alternative way to detect solid soil and pore space in CT images. The percentages of correct classifications of pore space of the total number of classifications among the tested images were 97.01%, 96.47% and 96.12%.
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Novikov, D. V., B. Adams, T. Hiort, E. Kossel, G. Materlik, R. Menk, and A. Walenta. "X-ray Holography for Structural Imaging." Journal of Synchrotron Radiation 5, no. 3 (May 1, 1998): 315–19. http://dx.doi.org/10.1107/s0909049597020153.
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X-ray atomic resolution holography is a new method for direct evaluation of three-dimensional electron density distribution in solids. The practical implementation of the multiple-energy technique on a synchrotron radiation source as well as image reconstruction from the experimental data are described. Holograms at several different energies were processed together to suppress twin images and artifacts from long-range-order effects in the experimental data sets. Reconstructed images of copper atoms in Cu2O crystals are presented.
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Gillibert, Luc, and Dominique Jeulin. "3D RECONSTRUCTION AND ANALYSIS OF THE FRAGMENTED GRAINS IN A COMPOSITE MATERIAL." Image Analysis & Stereology 32, no. 2 (June 25, 2013): 107. http://dx.doi.org/10.5566/ias.v32.p107-115.
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X-ray microtomography from solid propellant allows studying the microstructure of fragmented grains in damaged samples. A new reconstruction algorithm of fragmented grains for 3D images is introduced. Based on a watershed transform of a morphological closing of the input image, the algorithm can be used with different sets of markers. Two of them are compared. After the grain reconstruction, a multiscale segmentation algorithm is used to extract each fragment of the damaged grains. This allows an original quantitative study of the fragmentation of each grain in 3D. Experimental results on X-ray microtomographic images of a solid propellant fragmented under compression are presented and validated.
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MOSNEGUTU, EMILIAN-FLORIN, NARCIS BARSAN, ALEXANDRA-DANA CHITIMUS, CLAUDIA TOMOZEI, and MIHAIL RISTEA. "EXPERIMENTAL EVALUATION OF THE SOLID PARTICLES BEHAVIOR IN A VERTICAL AIR FLOW BY USING IMAGING ANALYSIS." Journal of Engineering Studies and Research 26, no. 4 (January 8, 2021): 61–68. http://dx.doi.org/10.29081/jesr.v26i4.237.
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To analyse the behavior of a solid particle in a vertical ascending air flow a series of studies have been carried out, both theoretical and experimental. This article presents a new method of imaging analysis of the behavior of a solid particle, in order to extend this study through experimental applications. The working algorithm implies the analysis of images in different positions of the solid particle in the vertical ascending air flow, analysis in relation to a reference image. By using the mathematical apparatus, i.e. Mathcad software, the movement of the solid particle in the air flow has been emphasized.
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Shapiro, V., and D. L. Vossler. "Efficient CSG Representations of Two-Dimensional Solids." Journal of Mechanical Design 113, no. 3 (September 1, 1991): 292–305. http://dx.doi.org/10.1115/1.2912782.
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Good methods are known for converting a Constructive Solid Geometry (CSG) representation of a solid into a boundary representation (b-rep) of the solid, but not for performing the inverse conversion, b-rep→CSG, which is the subject of this paper. Important applications of b-rep→CSG conversion arise in solid modeling, image processing, and elsewhere. The problem can be divided into two tasks: (1) finding a set of halfspaces that is necessary and sufficient (but not unique) to represent a given solid, and (2) constructing an efficient CSG representation using those halfspaces. This paper solves the problem for curved planar solids, i.e., r-sets in E2, with or without holes, whose boundary is given by a collection of edges. The edges may be subsets of straight lines or convex curves (i.e., curves which intersect any line in at most two points). We prove a number of results and describe algorithms that have been fully implemented for solids bounded by line segments and circular arcs. Empirical results show that the computed CSG representations are superior to those produced by earlier algorithms, and produce superior three-dimensional CSG representations for mechanical parts defined by contour sweeping. A companion paper generalizes the results to higher dimensional solids.
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Li, Gongcheng, Shulong Liu, Zengsheng Wen, Guolei Liu, Yu Cui, and Yajian Shao. "Effect of Ultrasonic Frequency on Thickener Performance." Advances in Materials Science and Engineering 2021 (July 29, 2021): 1–12. http://dx.doi.org/10.1155/2021/6624704.
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Gravity thickening is an important aspect to solve numerous environmental and safety problems that were created by tailings discharging at low solid concentrations. Furthermore, in order to efficiently facilitate the separation of released water and solid sediments, a continuous thickening system with ultrasonic equipment has been used to investigate the thickening performance of copper-mine tailings under different ultrasonic frequencies (16 kHz, 20 kHz, 22 kHz, 25 kHz, and 28 kHz). After freeze-drying treatment, the underflow samples are imaged using the scanning electron microscope (SEM); then, the structure of floc or aggregates in the SEM images is quantifiably analyzed using the software of Image J. Results show that the underflow concentration increases as the ultrasonic frequency increases and decreases afterwards. A linear logarithmic function can explain the relationship between underflow concentration and run time at a certain ultrasonic. The underflow concentration is maximized at 64.47 wt. % when the ultrasonic frequency is 22 kHz. Based on the analysis on the microstructure of underflow samples, the minimum pore average size and pore average fraction are obtained when the ultrasonic frequency is 22 kHz, implying that 22 kHz is the optimum ultrasonic frequency combining the results of the underflow concentration.
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Li, Ke, Jin Li Zhang, and Hai Xing Zhao. "The Design of Double Liquid Zoom Lens’ Model Based on Electrowetting Effect." Applied Mechanics and Materials 84-85 (August 2011): 436–41. http://dx.doi.org/10.4028/www.scientific.net/amm.84-85.436.
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The electrowetting effect is one physical chemistry phenomenon, it through adds certain external voltage in the solid - liquid contact surface to change the liquid to the solid moist degree, thus changing the contact angle of liquids and solids. In this article double liquid zoom lens are based on this kind of electrowetting effect. Optics focal variation system realizes the focal variation to satisfy two basic conditions[2]: (1) changing the focal distance; (2) maintaining the image plane position invariance. The single liquid zoom lens cannot maintain the position of the image formation surface invariance, this article takes on the electrowetting effect double liquid zoom lens as a foundation, and designs to realize new optical systems without mechanical movement focal variation using the motor control, and through MATLAB software simulation inferential reasoning the result.
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Dai, Junjie, Tianpeng Li, Zhaolong Xuan, and Zirui Feng. "Automated Defect Analysis System for Industrial Computerized Tomography Images of Solid Rocket Motor Grains Based on YOLO-V4 Model." Electronics 11, no. 19 (October 7, 2022): 3215. http://dx.doi.org/10.3390/electronics11193215.
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As industrial computerized tomography (ICT) is widely used in the non-destructive testing of a solid rocket motor (SRM), the problem of how to automatically discriminate defect types and measure defect sizes with high accuracy in ICT images of SRM grains needs to be urgently solved. To address the problems of low manual recognition efficiency and data utilization in the ICT image analysis of SRM grains, we proposed an automated defect analysis (ADA) system for ICT images of SRM grains based on the YOLO-V4 model. Using the region proposal of the YOLO-V4 model, a region growing algorithm with automatic selection of seed points was proposed to segment the defect areas of the ICT images of grains. Defect sizes were automatically measured based on the automatic determination of defect types by the YOLO-V4 model. In this paper, the image recognition performance of YOLO-V4, YOLO-V3, and Faster R-CNN models were compared. The results show that the average accuracy (mAP) of the YOLO-V4 model is more than 15% higher than that of the YOLO-V3 and Faster R-CNN models, the F1-score is 0.970, and the detection time per image is 0.152 s. The ADA system can measure defect sizes with an error of less than 10%. Tests show that the system proposed in this paper can automatically analyze the defects in ICT images of SRM grains and has certain application value.
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Collet, M. G. "Solid-state image sensors." Sensors and Actuators 10, no. 3-4 (November 1986): 287–302. http://dx.doi.org/10.1016/0250-6874(86)80051-8.
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Nnamoko, Nonso, Joseph Barrowclough, and Jack Procter. "Solid Waste Image Classification Using Deep Convolutional Neural Network." Infrastructures 7, no. 4 (March 25, 2022): 47. http://dx.doi.org/10.3390/infrastructures7040047.
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Separating household waste into categories such as organic and recyclable is a critical part of waste management systems to make sure that valuable materials are recycled and utilised. This is beneficial to human health and the environment because less risky treatments are used at landfill and/or incineration, ultimately leading to improved circular economy. Conventional waste separation relies heavily on manual separation of objects by humans, which is inefficient, expensive, time consuming, and prone to subjective errors caused by limited knowledge of waste classification. However, advances in artificial intelligence research has led to the adoption of machine learning algorithms to improve the accuracy of waste classification from images. In this paper, we used a waste classification dataset to evaluate the performance of a bespoke five-layer convolutional neural network when trained with two different image resolutions. The dataset is publicly available and contains 25,077 images categorised into 13,966 organic and 11,111 recyclable waste. Many researchers have used the same dataset to evaluate their proposed methods with varying accuracy results. However, these results are not directly comparable to our approach due to fundamental issues observed in their method and validation approach, including the lack of transparency in the experimental setup, which makes it impossible to replicate results. Another common issue associated with image classification is high computational cost which often results to high development time and prediction model size. Therefore, a lightweight model with high accuracy and a high level of methodology transparency is of particular importance in this domain. To investigate the computational cost issue, we used two image resolution sizes (i.e., 225×264 and 80×45) to explore the performance of our bespoke five-layer convolutional neural network in terms of development time, model size, predictive accuracy, and cross-entropy loss. Our intuition is that smaller image resolution will lead to a lightweight model with relatively high and/or comparable accuracy than the model trained with higher image resolution. In the absence of reliable baseline studies to compare our bespoke convolutional network in terms of accuracy and loss, we trained a random guess classifier to compare our results. The results show that small image resolution leads to a lighter model with less training time and the accuracy produced (80.88%) is better than the 76.19% yielded by the larger model. Both the small and large models performed better than the baseline which produced 50.05% accuracy. To encourage reproducibility of our results, all the experimental artifacts including preprocessed dataset and source code used in our experiments are made available in a public repository.
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Nagumo, Fumio. "High-resolution solid state image sennsor. Application of solid state image sensor." Journal of the Institute of Television Engineers of Japan 44, no. 2 (1990): 132–38. http://dx.doi.org/10.3169/itej1978.44.132.
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Soong, David, David Soong, David Soong, Anantharaman Muthuswamy, Clifton Drew, Nora Pencheva, Maria Jure-Kunkel, et al. "833 A scalable deep learning framework for rapid automated annotation of histologic and morphologic features from large unlabeled pan-cancer H&E datasets." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (November 2021): A874. http://dx.doi.org/10.1136/jitc-2021-sitc2021.833.
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BackgroundRecent advances in machine learning and digital pathology have enabled a variety of applications including predicting tumor grade and genetic subtypes, quantifying the tumor microenvironment (TME), and identifying prognostic morphological features from H&E whole slide images (WSI). These supervised deep learning models require large quantities of images manually annotated with cellular- and tissue-level details by pathologists, which limits scale and generalizability across cancer types and imaging platforms. Here we propose a semi-supervised deep learning framework that automatically annotates biologically relevant image content from hundreds of solid tumor WSI with minimal pathologist intervention, thus improving quality and speed of analytical workflows aimed at deriving clinically relevant features.MethodsThe dataset consisted of >200 H&E images across >10 solid tumor types (e.g. breast, lung, colorectal, cervical, and urothelial cancers) from advanced disease patients. WSI were first partitioned into small tiles of 128μm for feature extraction using a 50-layer convolutional neural network pre-trained on the ImageNet database. Dimensionality reduction and unsupervised clustering were applied to the resultant embeddings and image clusters were identified with enriched histological and morphological characteristics. A random subset of representative tiles (<0.5% of whole slide tissue areas) from these distinct image clusters was manually reviewed by pathologists and assigned to eight histological and morphological categories: tumor, stroma/connective tissue, necrotic cells, lymphocytes, red blood cells, white blood cells, normal tissue and glass/background. This dataset allowed the development of a multi-label deep neural network to segment morphologically distinct regions and detect/quantify histopathological features in WSI.ResultsAs representative image tiles within each image cluster were morphologically similar, expert pathologists were able to assign annotations to multiple images in parallel, effectively at 150 images/hour. Five-fold cross-validation showed average prediction accuracy of 0.93 [0.8–1.0] and area under the curve of 0.90 [0.8–1.0] over the eight image categories. As an extension of this classifier framework, all whole slide H&E images were segmented and composite lymphocyte, stromal, and necrotic content per patient tumor was derived and correlated with estimates by pathologists (p<0.05).ConclusionsA novel and scalable deep learning framework for annotating and learning H&E features from a large unlabeled WSI dataset across tumor types was developed. This automated approach accurately identified distinct histomorphological features, with significantly reduced labeling time and effort required for pathologists. Further, this classifier framework was extended to annotate regions enriched in lymphocytes, stromal, and necrotic cells – important TME contexture with clinical relevance for patient prognosis and treatment decisions.
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Tariq, Mashal, Ayesha A. Siddiqi, Ghous Baksh Narejo, and Shehla Andleeb. "A Cross Sectional Study of Tumors Using Bio-Medical Imaging Modalities." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 1 (December 7, 2018): 66–73. http://dx.doi.org/10.2174/1573405613666170614081434.
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Background: Digital Signal Processing (D.S.P) is an evolutionary field. It has a vast variety of applications in all fields. Bio medical engineering has various applications of digital signal processing. Digital Image Processing is one of the branches of signal processing. Medical image modalities proved to be helpful for disease diagnosis. Higher expertise is required in image analysis by medical professional, either doctors or radiologists. Methods: Extensive research is being done and has produced remarkable results. The study is divided into three main parts. The first deals with introduction of mostly used imaging modalities such as, magnetic resonance imaging, x-rays, ultrasound, positron emission tomography and computed tomography. The next section includes explanation of the basic steps of digital image processing are also explained in the paper. Magnetic Resonance imaging modalities is selected for this research paper. Different methods are tested on MRI images. Discussion: Brain images are selected with and without tumor. Solid cum Cystic tumor is opted for the r esearch. Results are discussed and shown. The software used for digital image processing is MATLAB. It has in built functions which are used throughout the study. The study represents the importance of DIP for tumor segmentation and detection. Conclusion: This study provides an initial guideline for researchers from both fields, that is, medicine and engineering. The analyses are shown and discussed in detail through images. This paper shows the significance of image processing platform for tumor detection automation.
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Winter, John M., and Robert E. Green. "Real Time Synchrotron Topography Using a CID Array Camera with Digital Image Acquisition and Processing." Advances in X-ray Analysis 38 (1994): 215–19. http://dx.doi.org/10.1154/s037603080001781x.
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Synchrotron white beam transmission topography of GaAs as previously reported by the authors relied on scanning specimen and film synchronously through the incident x-ray beam to record transmission topographic images en film. Sometimes the total dose required for reasonable contrast on film carried with it enough thermal deposition to cause elastic warping of the wafer. To escape these problems, a real time system was assembled. This system included an image intensifier, a solid state camera, a computer board to frame-grab and digitize images, and appropriate image processing software. With this system, a three inch specimen was scanned from edge to edge in one minute. At this scan rate, the incident x-ray beam had to be significantly attenuated to avoid saturating the intensifier output.
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Papadakis, Antonios E., and John Damilakis. "Evaluation of an organ-based tube current modulation tool in pediatric CT examinations." European Radiology 30, no. 10 (May 20, 2020): 5728–37. http://dx.doi.org/10.1007/s00330-020-06888-5.
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Abstract Objectives To investigate the effect of an organ-based tube current modulation (OTCM) technique on organ absorbed dose and assess image quality in pediatric CT examinations. Methods Four physical anthropomorphic phantoms that represent the average individual as neonate, 1-year-old, 5-year-old, and 10-year-old were used. Standard head and thorax acquisitions were performed with automatic tube current modulation (ATCM) and ATCM+OTCM. Dose calculations were performed by means of Monte Carlo simulations. Radiation dose was measured for superficial and centrally located radiosensitive organs. The angular range of the OTCM exposure window was determined for different tube rotation times (t) by means of a solid-state detector. Image noise was measured as the standard deviation of the Hounsfield unit value in regions of interest drawn at selected anatomical sites. Results ATCM+OTCM resulted in a reduction of radiation dose to all radiosensitive organs. In head, eye lens dose was reduced by up to 13% in ATCM+OTCM compared with ATCM. In thorax, the corresponding reduction for breast dose was up to 10%. The angular range of the OTCM exposure window decreased with t. For t = 0.4 s, the angular range was limited to 74° in head and 135° for thorax. Image noise was significantly increased in ATCM+OTCM acquisitions across most examined phantoms (p < 0.05). Conclusions OTCM reduces radiation dose to exposed radiosensitive organs with the eye lens and breast buds exhibiting the highest dose reduction. The OTCM exposure window is narrowed at short t. An increase in noise is inevitable in images located within the OTCM-activated imaged volume. Key Points • In pediatric CT, organ-based tube current modulation reduces radiation dose to all major primarily exposed radiosensitive organs. • Image noise increases within the organ-based tube current modulation enabled imaged volume. • The angular range of the organ-based tube current modulation low exposure window is reduced with tube rotation time.