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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.
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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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.
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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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.
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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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.
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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Poeckes, E., F. Ries, M. Dicato, and R. Dondelinger. "Image cytometry (IC) in solid tumors." European Journal of Cancer and Clinical Oncology 27 (January 1991): S84. http://dx.doi.org/10.1016/0277-5379(91)91556-x.
Radice, A., S. Malavasi, and F. Ballio. "Solid transport measurements through image processing." Experiments in Fluids 41, no. 5 (September 13, 2006): 721–34. http://dx.doi.org/10.1007/s00348-006-0195-9.
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.
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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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.
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.
Amplification of low-light-level optical images is important for extending the range of lidar systems that image and detect objects in the atmosphere and underwater. The use of range-gating to produce images of particular range bins is also important in minimizing the image degradation due to light that is scattered backward from aerosols, smoke, or water along the imaging path. For practical lidar systems that must be operated within sight of unprotected observers, eye safety is of the utmost importance. This dissertation describes a new type of eye-safe, range-gated lidar sensing element based on Solid-state Raman Image Amplification (SSRIA) in a solid-state optical crystal. SSRIA can amplify low-level images in the eye-safe infrared at 1.556 μm with gains up to 106 with the addition of only quantum-limited noise. The high gains from SSRIA can compensate for low quantum efficiency detectors and can reduce the need for detector cooling. The range-gate of SSRIA is controlled by the pulsewidth of the pump laser and can be as short as 30-100 cm, using pump pulses of 2-6.7 nsec FWHM. A rate equation theoretical model is derived to help in the design of short pulsed Raman lasers. A theoretical model for the quantum noise properties of SSRIA is presented. SSRIA results in higher SNR images throughout a broad range of incident light levels, in contrast to the increasing noise factor with reduced gain in image intensified CCD's. A theoretical framework for the optical resolution of SSRIA is presented and it is shown that SSRIA can produce higher resolution than ICCD's. SSRIA is also superior in rejecting unwanted sunlight background, further increasing image SNR. Lastly, SSRIA can be combined with optical pre-filtering to perform optical image processing functions such as high-pass filtering and automatic target detection/recognition. The application of this technology to underwater imaging, called Marine Raman Image Amplification (MARIA) is also discussed. MARIA operates at a wavelength of 563 nm, which passes most efficiently through coastal ocean waters. The imaging resolution of MARIA in the marine environment can be superior to images produced by laser line scan or standard range-gated imaging systems.
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McLean, Alan Stuart. "Transfer matrices and image transport in random media." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307659.
Lewis, Skigh E. "Laser Levitation of Solid Particles for Combustion and Gasification Applications." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd2859.pdf.
Holliman, Nicolas S. "Visualising solid models : an exercise in parallel programming." Thesis, University of Leeds, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277611.
Hjipieris, G. "Millimetre wave six-port reflectometry using image and dielectric guides." Thesis, University of Kent, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.354575.
Kalfoutzos, Aristeidis. "Free electron and solid state lasers development for naval directed energy." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Dec%5FKalfoutzos.pdf.
Thesis (M.S. in Applied Physics and M.S. in Electrical Engineering)--Naval Postgraduate School, December 2002. Thesis advisor(s): William B. Colson, Phillip E. Pace. Includes bibliographical references (p. 101-102). Also available online.
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Goodman, Benjamin T. "Thermodynamic Property Prediction for Solid Organic Compounds Based on Molecular Structure." Diss., CLICK HERE for online access, 2003. http://contentdm.lib.byu.edu/ETD/image/etd300.pdf.
Beutler, Joshua L. "Frequency Response and Gain Enhancement of Solid-State Impact-Ionization Multipliers (SIMs)." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3378.pdf.
Darmiati, Tience. "Maximising the potential for community-based solid waste management in Indonesia /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18392.pdf.
A new approach to image-space hardware-based rendering of Constructive Solid Geometry (CSG) models is presented. The work is motivated by the evolving functionality and performance of computer graphics hardware. This work is also motivated by a specific industrial application --- interactive verification of five axis grinding machine tool programs. The goal is to minimise the amount of time required to render each frame in an animation or interactive application involving boolean combinations of three dimensional shapes. The Sequenced Convex Subtraction (SCS) algorithm utilises sequenced subtraction of convex objects for the purpose of interactive CSG rendering. Concave shapes must be decomposed into convex shapes for the purpose of rendering. The length of Permutation Embedding Sequences (PESs) used as subtraction sequences are shown to have a quadratic lower bound. In many situations shorter sequences can be used, in the best case linear. Approaches to s ubtraction sequence encoding are presented including the use of object-space overlap information. The implementation of the algorithm is experimentally shown to perform better on modern commodity graphics hardware than previously reported methods. This work also examines performance aspects of the SCS algorithm itself. Overall performance depends on hardware characteristics, the number and spatial arrangement of primitives, and the structure and boolean operators of the CSG tree.
International Conference on Defect Recognition and Image Processing in Semiconductors (7th 1997 Templin, Germany). Defect recognition and image processing in semiconductors 1997: Proceedings of the 7th International Conference on Defect Recognition and Image Processing in Semiconductors (DRIP VII) held in Templin, Germany, 7-10 September 1997. Bristol: Institute of Physics Pub., 1998.
IUTAM Symposium on Advanced Optical Methods and Applications in Solid Mechanics (1998 Poitiers, France). IUTAM Symposium on Advanced Optical Methods and Applications in Solid Mechanics: Proceedings of the IUTAM Symposium held in Futuroscope, Poitiers, France August 31st - September 4th 1998. Dordrecht: Kluwer Academic Publishers, 2000.
Gdoutos, Emmanuel E. "Digital Image Correlation (DIC)." In Solid Mechanics and Its Applications, 251–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89466-5_12.
Zhang, Kun, Shi Pan, Shi Fa Wu, and Ming Gao. "A New Method to Obtain Tip Shape from Images and Reconstruct the Corresponding Image." In Solid State Phenomena, 873–76. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/3-908451-30-2.873.
Zitkevicius, Evaras, and Romanas Martavicius. "Image Wavelet Transform for PCB Soldering's Quality Evaluation." In Solid State Phenomena, 85–90. Stafa: Trans Tech Publications Ltd., 2006. http://dx.doi.org/10.4028/3-908451-21-3.85.
Sadovnikov, Albert, Petja Salmela, Lasse Lensu, Joni-Kristian Kamarainen, and Heikki Kälviäinen. "Mottling Assessment of Solid Printed Areas and Its Correlation to Perceived Uniformity." In Image Analysis, 409–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_42.
Wang, Weixing, and BingCui. "Solid Particle Measurement by Image Analysis." In Lecture Notes in Computer Science, 578–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881223_72.
Joy, David C. "Ion–Solid Interactions and Image Formation." In SpringerBriefs in Materials, 17–37. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8660-2_4.
Lee, Sang Joo, Seung Woo Han, Jae Hyun Kim, and Hak Joo Lee. "Measurement of Mechanical Properties for Thin Film Using Visual Image Tracing Method." In Solid State Phenomena, 1701–4. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/3-908451-31-0.1701.
Cristofolini, Luca. "Overview of Digital Image Correlation." In Springer Series in Solid and Structural Mechanics, 187–213. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-06086-6_5.
Collet, M. G. "Solid State Image Sensors." In 1985 International Technical Symposium/Europe, edited by Gilbert J. Declerck. SPIE, 1986. http://dx.doi.org/10.1117/12.952082.
Calmes, Lonnie K., James T. Murray, William L. Austin, and Richard C. Powell. "Solid state Raman image amplification." In Aerospace/Defense Sensing and Controls, edited by Ram M. Narayanan and James E. Kalshoven, Jr. SPIE, 1998. http://dx.doi.org/10.1117/12.312629.
Chung, Tien-Tung, Li-Chang Chuang, Jhe-Wei Lee, and Shun-Hsiung Hsu. "Solid model reconstruction from triangular meshes." In Second International Conference on Digital Image Processing. SPIE, 2010. http://dx.doi.org/10.1117/12.856004.
Zhou, Chi, Yong Chen, and Richard A. Waltz. "Optimized Mask Image Projection for Solid Freeform Fabrication." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86268.
Solid freeform fabrication (SFF) processes based on mask image projection have the potential to be fast and inexpensive. More and more research and commercial systems have been developed based on these processes. For the SFF processes, the mask image planning is an important process planning step. In this paper, we present an optimization based method for mask image planning. It is based on a light intensity blending technique called pixel blending. By intelligently controlling pixels’ gray scale values, the SFF processes can achieve a much higher XY resolution and accordingly better part quality. We mathematically define the pixel blending problem and discuss its properties. Based on the formulation, we present several optimization models for solving the problem including a mixed integer programming model, a linear programming model, and a two-stage optimization model. Both simulated and physical experiments for various CAD models are presented to demonstrate the effectiveness and efficiency of our method.
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Lo, Yawcheng. "Solid state image sensor: technologies and applications." In Asia Pacific Symposium on Optoelectronics '98, edited by Yusheng Tim Tsai, Teh-Ming Kung, and Jan Larsen. SPIE, 1998. http://dx.doi.org/10.1117/12.311105.
Chang, W.-C., G. R. Moore, K. W. Wong, T. H. Lee, B. C. Burkey, and R. P. Khosla. "A Four-Megapixel Solid-State Image Sensor." In 33rd Annual Techincal Symposium, edited by John E. Wampler. SPIE, 1989. http://dx.doi.org/10.1117/12.962687.
Teranishi, Nobukazu, and Hiroshi Tanigawa. "Integrated photodiode arrays: solid state image sensors." In 14th Symposium of the TC2 on Photonic Measurements, edited by Janos Schanda and Tivadar Lippenyi. SPIE, 1993. http://dx.doi.org/10.1117/12.140174.
Cuenca, Miguel, Haroldo Maestre, Adrián J. Torregrosa, and Juan Capmany. "External-Cavity Yb:KGW IR-to-Visible Image Upconverter." In Advanced Solid State Lasers. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/assl.2019.jth3a.35.
Kenes, Bulent. Per Jimmie Åkesson: A Smiling Wolf in Sheep’s Clothing? European Center for Populism Studies (ECPS), August 2020. http://dx.doi.org/10.55271/lp0002.
Jimmie Åkesson and his party, the Sweden Democrats (SD), are not yet authentically democratic. They are still “the same old iron gang as usual” despite concerted efforts to change their image. Of course, Åkesson has steered the SD away from the Nazi movement onto a more parliamentary path. But its essence – alarmist resistance to immigrants and Islamophobia – has remained the same, and there is still no solid indication that Åkesson has matured or moderated over the years.
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Habib, Ayman, Darcy M. Bullock, Yi-Chun Lin, and Raja Manish. Road Ditch Line Mapping with Mobile LiDAR. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317354.
Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires mapping of the ditch profile to identify areas requiring excavation of long-term sediment accumulation. High-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) provide an opportunity for effective monitoring of roadside ditches and performing hydrological analyses. This study evaluated the applicability of mobile LiDAR for mapping roadside ditches for slope and drainage analyses. The performance of alternative MLMS units was performed. These MLMS included an unmanned ground vehicle, an unmanned aerial vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system. Point cloud from all the MLMS units were in agreement in the vertical direction within the ±3 cm range for solid surfaces, such as paved roads, and ±7 cm range for surfaces with vegetation. The portable backpack system that could be carried by a surveyor or mounted on a vehicle and was the most flexible MLMS. The report concludes that due to flexibility and cost effectiveness of the portable backpack system, it is the preferred platform for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground filtering approach is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data was found to be very close to highway cross slope design standards of 2% on driving lanes, 4% on shoulders, as well as 6-by-1 slope for ditch lines. Potential flooded regions are identified by detecting areas with no LiDAR return and a recall score of 54% and 92% was achieved by the medium-grade wheel-based and vehicle-mounted portable systems, respectively. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground filtering approach is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from LiDAR data, and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data was found to be very close to highway cross slope design standards of 2% on driving lanes, 4% on shoulder, as well as 6-by-1 slope for ditch lines. Potential flooded regions are identified by detecting areas with no LiDAR return and a recall score of 54% and 92% was achieved by the medium-grade wheel-based and vehicle-mounted portable systems, respectively.
