Artificial Neural Networks for Computer Vision by Yi-Tong Zhou

By Yi-Tong Zhou

This monograph is an outgrowth of the authors' fresh examine at the de­ velopment of algorithms for numerous low-level imaginative and prescient difficulties utilizing man made neural networks. particular difficulties thought of are static and movement stereo, computation of optical circulate, and deblurring a picture. From a mathematical perspective, those inverse difficulties are ill-posed based on Hadamard. Researchers in machine imaginative and prescient have taken the "regularization" method of those difficulties, the place one comes up with a suitable strength or price functionality and reveals a minimal. extra constraints comparable to smoothness, integrability of surfaces, and renovation of discontinuities are extra to the fee functionality explicitly or implicitly. reckoning on the character of the inver­ sion to be played and the restrictions, the price functionality may possibly show numerous minima. Optimization of such nonconvex services could be very concerned. even though development has been made in making innovations akin to simulated annealing computationally extra moderate, it truly is our view that one could frequently locate passable options utilizing deterministic optimization algorithms.

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In this chapter, we present two neural network-based algorithms, batch and recursive, for computing the disparity field using a sequence of lateral motion images. The batch approach first determines the bias inputs by using all image frames. A disparity field is then computed by the neural network. Since the batch approach computes the disparity field only once, computational complexity is reduced in comparison with existing approaches. The recursive approach uses a recursive least squares (RLS) algorithm to update the bias inputs instead of updating disparity values.

For example, the (i, j)th pixel moves from position (i, j) in the first frame to position (i, j + in the second frame. Let ',3 Si,j (p) be the total shift of pixel (i, j) from the first frame to the pth frame. 14) is the true disparity value for pixel (i,j). Note that the shift is continuous due to the continuous variable d{,j. A rounding operation has to be applied to Si,j(P) for locating the (i,j)th pixel in the where Si,j(u) di,j 4. Motion Stereo-Lateral Motion 51 subsampled image. 15) where [ ] is a rounding operator.

1. 1. Camera geometry for motion stereo. no smoothing procedure is needed. Since the neural network can be run in parallel and the RLS algorithm can be implemented on line, this approach is extremely fast and hence useful for real time robot vision applications. As the derivatives of the intensity function are more reliable than the intensity values and are also dense, both approaches use the derivatives as measurement primitives for matching. The Chamfer distance information is also used for matching to overcome the lack of information in homogeneous regions.

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