Статті в журналах: "Solid image"

1

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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2

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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3

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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4

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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5

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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6

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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7

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.

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8

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.

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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.

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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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10

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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11

Hirota, Yoshihiro, Toshiyuki Isshiki, and Makoto Shiojiri. "Crystal structures and transformation of Cu-Se and Ag-Te in solid-solid reactions." Proceedings, annual meeting, Electron Microscopy Society of America 48, no. 4 (August 1990): 352–53. http://dx.doi.org/10.1017/s0424820100174898.

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Cu-Se and Ag-Te crystals which grew by solid-solid reactions of vacuum-deposited metal and chalcogen films were investigated with a JEM 200-CX electron microscope (Cs= 1.2 mm), with the aid of image simulation and optical diffraction.Fig. 1 shows an image of a slender crystal observed 10 days after an amorphous Se film was mounted on a holey Cu film. The image in A matches with a calculated image shown in Fig. 2a. The calculation was carried out for a CuSe crystal (P63/mmc, a=0.394, c=1.725 nm) 20 nm thick, with the a-axis relatively tilted at 1° about the [012] axis to the incident electron beam, at an underfocus of Δf=50 nm. The optical diffractogram from the image of the area A was used for the calibration of the distance in the optical diffractograms from the other areas B∼ E. The crystal in B formed before the formation of the CuSe crystal.

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12

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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13

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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14

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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15

Munaretto, D., and M. Roggero. "SOLID IMAGE EXTRACTION FROM LIDAR POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W1 (February 13, 2013): 189–95. http://dx.doi.org/10.5194/isprsarchives-xl-5-w1-189-2013.

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16

Hynecek, J. "Impactron-a new solid state image intensifier." IEEE Transactions on Electron Devices 48, no. 10 (2001): 2238–41. http://dx.doi.org/10.1109/16.954460.

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17

Griffin, Lewis D. "The Second Order Local-Image-Structure Solid." IEEE Transactions on Pattern Analysis and Machine Intelligence 29, no. 8 (August 2007): 1355–66. http://dx.doi.org/10.1109/tpami.2007.1066.

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18

Frank, Darya, Oliver Gray, and Daniela Montaldi. "SOLID-Similar object and lure image database." Behavior Research Methods 52, no. 1 (February 25, 2019): 151–61. http://dx.doi.org/10.3758/s13428-019-01211-7.

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19

Du, Song-Pei, Shi-Min Hu, and Ralph R. Martin. "Semiregular Solid Texturing from 2D Image Exemplars." IEEE Transactions on Visualization and Computer Graphics 19, no. 3 (March 2013): 460–69. http://dx.doi.org/10.1109/tvcg.2012.129.

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20

Fossum, Eric R., Jerry Hynecek, John Tower, Nobukazu Teranishi, Junichi Nakamura, Pierre Magnan, and Albert J. P. Theuwissen. "Special Issue on Solid-State Image Sensors." IEEE Transactions on Electron Devices 56, no. 11 (November 2009): 2376–79. http://dx.doi.org/10.1109/ted.2009.2031900.

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21

Komov, M. E., and Yu S. Popkov. "Ultrasound ‘image’ of defects in a solid." Welding International 17, no. 3 (January 2003): 244–46. http://dx.doi.org/10.1533/wint.2003.3108.

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22

Savadjiev, Peter, Jaron Chong, Anthony Dohan, Vincent Agnus, Reza Forghani, Caroline Reinhold, and Benoit Gallix. "Image-based biomarkers for solid tumor quantification." European Radiology 29, no. 10 (April 8, 2019): 5431–40. http://dx.doi.org/10.1007/s00330-019-06169-w.

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23

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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24

OHSHIMA, S., M. TAKANO, T. MAKINO, and M. NAKAMURA. "OH airglow observation by image intensifier combined with solid-state image sensor." Journal of geomagnetism and geoelectricity 38, no. 8 (1986): 771–77. http://dx.doi.org/10.5636/jgg.38.771.

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25

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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26

Ando, Fumihiko. "High-resolution solid state image sennsor. Multi-functional solid state imaging techniques." Journal of the Institute of Television Engineers of Japan 44, no. 2 (1990): 127–31. http://dx.doi.org/10.3169/itej1978.44.127.

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27

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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28

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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29

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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30

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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Анотація:

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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31

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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32

Hamamoto, Takayuki, Kiyoharu Aizawa, and Mitsutoshi Hatori. "Solid State Imaging Techniques. Motion Adaptive Image Sensor." Journal of the Institute of Image Information and Television Engineers 51, no. 2 (1997): 277–79. http://dx.doi.org/10.3169/itej.51.277.

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33

Matsumoto, Kazuya. "Solid State Image Sensors in Vidual Spectral Range." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 78, no. 3 (1994): 96–99. http://dx.doi.org/10.2150/jieij1980.78.3_96.

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34

Willemin, M., N. Blanc, G. K. Lang, S. Lauxtermann, P. Schwider, P. Seitz, and M. Wäny. "Optical characterization methods for solid-state image sensors." Optics and Lasers in Engineering 36, no. 2 (August 2001): 185–94. http://dx.doi.org/10.1016/s0143-8166(01)00035-5.

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35

Nishida, Yasuaki, Junro Koike, Hiroshi Ohtake, Toshihide Watanabe, and Shigeo Yoshikawa. "A new architecture for solid-state image sensors." Journal of the Institute of Television Engineers of Japan 41, no. 11 (1987): 1061–67. http://dx.doi.org/10.3169/itej1978.41.1061.

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36

Huttenlocher, Daniel P., and Shimon Ullman. "Recognizing solid objects by alignment with an image." International Journal of Computer Vision 5, no. 2 (November 1990): 195–212. http://dx.doi.org/10.1007/bf00054921.

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37

Chang, W. C., T. J. Tredwell, E. G. Stevens, and D. N. Nichols. "Technical Note: High-Density Solid-State Image Sensor." SMPTE Journal 96, no. 12 (December 1987): 1186–88. http://dx.doi.org/10.5594/j02992.

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38

Pimbley, J. M., and G. J. Michon. "Charge detection modeling in solid-state image sensors." IEEE Transactions on Electron Devices 34, no. 2 (February 1987): 294–300. http://dx.doi.org/10.1109/t-ed.1987.22921.

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39

Pedersen, Christian, Emir Karamehmedović, Jeppe Seidelin Dam, and Peter Tidemand-Lichtenberg. "Enhanced 2D-image upconversion using solid-state lasers." Optics Express 17, no. 23 (October 30, 2009): 20885. http://dx.doi.org/10.1364/oe.17.020885.

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40

Fossum, E. R., N. Teranishi, A. J. P. Theuwissen, and J. Hynecek. "Foreword special issue on solid-state image sensors." IEEE Transactions on Electron Devices 50, no. 1 (January 2003): 1–3. http://dx.doi.org/10.1109/ted.2002.807524.

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41

Goto, Hiroshige. "High-resolution solid state image sennsor. Advances in high-resolution linear image sensors." Journal of the Institute of Television Engineers of Japan 44, no. 2 (1990): 122–26. http://dx.doi.org/10.3169/itej1978.44.122.

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Liu, Sheng, Yuan Feng, Shaobo Zhang, Hongzhang Song, and Shengyong Chen. "$L_0$ Sparse Regularization-Based Image Blind Deblurring Approach for Solid Waste Image Restoration." IEEE Transactions on Industrial Electronics 66, no. 12 (December 2019): 9837–45. http://dx.doi.org/10.1109/tie.2019.2892681.

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43

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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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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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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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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Wertheim, D., G. Gillmore, L. Brown, and N. Petford. "3-D imaging of particle tracks in solid state nuclear track detectors." Natural Hazards and Earth System Sciences 10, no. 5 (May 20, 2010): 1033–36. http://dx.doi.org/10.5194/nhess-10-1033-2010.

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Abstract. It has been suggested that 3 to 5% of total lung cancer deaths in the UK may be associated with elevated radon concentration. Radon gas levels can be assessed using CR-39 plastic detectors which are often assessed by 2-D image analysis of surface images. 3-D analysis has the potential to provide information relating to the angle at which alpha particles impinge on the detector. In this study we used a "LEXT" OLS3100 confocal laser scanning microscope (Olympus Corporation, Tokyo, Japan) to image tracks on five CR-39 detectors. We were able to identify several patterns of single and coalescing tracks from 3-D visualisation. Thus this method may provide a means of detailed 3-D analysis of Solid State Nuclear Track Detectors.

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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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Yamada, Eiji, Masayuki Nishikawa, Toshiaki Harada, Hideo Okada, Tetsuo Iwaki, and Tohru Okuda. "Solid State Imaging Techniques. Improvement of Image of Bayer Arrangement CCD using Image-shift." Journal of the Institute of Image Information and Television Engineers 53, no. 2 (1999): 295–301. http://dx.doi.org/10.3169/itej.53.295.

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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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