Artificial Neural Networks for Computer Vision - download pdf or read online

By Yi-Tong Zhou

ISBN-10: 0387976833

ISBN-13: 9780387976839

ISBN-10: 1461228344

ISBN-13: 9781461228349

This monograph is an outgrowth of the authors' fresh study at the de­ velopment of algorithms for a number of low-level imaginative and prescient difficulties utilizing synthetic neural networks. particular difficulties thought of are static and movement stereo, computation of optical circulation, and deblurring a picture. From a mathematical perspective, those inverse difficulties are ill-posed in response to Hadamard. Researchers in laptop imaginative and prescient have taken the "regularization" method of those difficulties, the place one comes up with a suitable strength or rate functionality and unearths a minimal. extra constraints reminiscent of smoothness, integrability of surfaces, and protection of discontinuities are extra to the price functionality explicitly or implicitly. counting on the character of the inver­ sion to be played and the limitations, the associated fee functionality might convey a number of minima. Optimization of such nonconvex services may be very concerned. even though growth has been made in making recommendations resembling simulated annealing computationally extra moderate, it truly is our view that you can actually frequently locate passable recommendations utilizing deterministic optimization algorithms.

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25) yEO where the ",,)' denotes the estimated intensity derivative using the sampled intensity functions. 26) iEO where u( i) is a step function 1, u(i) = { -1, i>O < O. 11), which means it is a correct estimate. 3. Estimation of Intensity Derivatives caused by the spatial quantization error. Since the spatial quantization error is proportional to f and d, and is inversely proportional to Z, the estimation error is small when the camera is close enough and/or the object' is far enough. When the surface is not parallel to the image plane and the object is close to the camera, using a large window to estimate the derivatives will give a large error due to the accumulated quantization error.

7(a). 7(c) was obtained after 12 asynchronous iterations. The same result can be obtained after 19 synchronous iterations. Another type of perturbation is Gaussian white noise [Jul60]. 8 shows a pair of multiple gray level random dot images with intensity value in the range of [0,255]. A 5 dB signal to noise ratio (SNR) Gaussian white noise was added to the left image. The SNR is defined as SNR = 10 loglO u; u -t Un 2 where u~ and are the variances of the original left image and noise. The parameters A = 450, D = 6 and w = 2 were used.

In this section, we derive a mean value for the matching error which can be used to detect the occluding pixels. 23) 1)8i ,jg" (i,j + (p - l)(kw + 8i ,j) + o(8r) l)(kw p = 2,3, ... 6. ) denotes the second order derivative. The best estimate of the disparity value is given by di,j = kw. Therefore, the matching error is m-l error = m ~ 1 L [g~(i,j + (p -l)kw) - g~+l(i,j + pkwW p=l m~1 m-l L [g~(i,j + (p -l)(kw + bi,j)) p=l -g~+l(i,j + p(kw + bi,j)) + (p - l)(kw + bi,j)) 2 +p bi,jgp+l(Z') + p(kw + bi,j))] .

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Artificial Neural Networks for Computer Vision by Yi-Tong Zhou


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