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Titlebook: Artificial Neural Networks for Computer Vision; Yi-Tong Zhou,Rama Chellappa Textbook 1992 Springer-Verlag New York, Inc. 1992 Stereo.algor

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期刊全稱Artificial Neural Networks for Computer Vision
影響因子2023Yi-Tong Zhou,Rama Chellappa
視頻videohttp://file.papertrans.cn/163/162672/162672.mp4
學(xué)科分類Research Notes in Neural Computing
圖書封面Titlebook: Artificial Neural Networks for Computer Vision;  Yi-Tong Zhou,Rama Chellappa Textbook 1992 Springer-Verlag New York, Inc. 1992 Stereo.algor
影響因子This monograph is an outgrowth of the authors‘ recent research on the de- velopment of algorithms for several low-level vision problems using artificial neural networks. Specific problems considered are static and motion stereo, computation of optical flow, and deblurring an image. From a mathematical point of view, these inverse problems are ill-posed according to Hadamard. Researchers in computer vision have taken the "regularization" approach to these problems, where one comes up with an appropriate energy or cost function and finds a minimum. Additional constraints such as smoothness, integrability of surfaces, and preservation of discontinuities are added to the cost function explicitly or implicitly. Depending on the nature of the inver- sion to be performed and the constraints, the cost function could exhibit several minima. Optimization of such nonconvex functions can be quite involved. Although progress has been made in making techniques such as simulated annealing computationally more reasonable, it is our view that one can often find satisfactory solutions using deterministic optimization algorithms.
Pindex Textbook 1992
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,Motion Stereo—Longitudinal Motion,vigation applications. Most existing algorithms have some problems associated with the location of the FOE, and with the camera and surface orientations. These problems limit their applicability to real scenes. It is highly desirable to develop an efficient and practical algorithm to infer depth inf
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Computation of Optical Flow,field, but this is not always true [Hor86]. It is common to assume that the optical flow is not too different from the motion field. Under this assumption, the optical flow can be used for segmenting images into regions and estimating the object motion in the scene [Adi85].
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Conclusions and Future Research,w, and image restoration. To ensure quick convergence of the networks, the deterministic decision rule was used in all the algorithms. Experimental results using natural images confirm that neural networks provide simple but very efficient means to solve computer vision problems, especially at the l
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Wissenschaftliche Taschenbücherrks. The task of low-level vision is to recover physical properties of visible three-dimensional surfaces from two-dimensional images. One module of low-level vision, for instance, extracts depth information from two eyes, making binocular images, or from one eye over a period of time, making a sequ
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