Adaptive Image Processing: A Computational Intelligence by Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau San Wong

By Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau San Wong

Illustrating crucial points of adaptive snapshot processing from a computational intelligence standpoint, the second one variation of Adaptive picture Processing: A Computational Intelligence viewpoint offers an authoritative and distinctive account of computational intelligence (CI) tools and algorithms for adaptive picture processing in regularization, part detection, and early vision.

With 3 new chapters and up-to-date info all through, the recent version of this renowned reference comprises enormous new fabric that specializes in purposes of complicated CI recommendations in photograph processing purposes. It introduces new thoughts and frameworks that display how neural networks, help vector machines, fuzzy common sense, and evolutionary algorithms can be utilized to handle new demanding situations in photograph processing, together with low-level photo processing, visible content material research, function extraction, and development recognition.

Emphasizing advancements in cutting-edge CI strategies, comparable to content-based photo retrieval, this publication keeps to supply educators, scholars, researchers, engineers, and technical managers in visible info processing with the updated knowing required to deal with modern demanding situations in snapshot content material processing and research.

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Extra resources for Adaptive Image Processing: A Computational Intelligence Perspective (2nd Edition)

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The final value of the pixel acted upon by this PSF is the sum of the values of each pixel under the PSF mask, each multiplied by the matching entry in the PSF mask. Consider a PSF of size P by P acting on an image of size N by M. Let L = MN. In the case of a two-dimensional image, the PSF may be written as h(x, y; α, β). The four sets of indices indicate that the PSF may be spatially variant, hence the PSF will be a different function for pixels in different locations of an image. 1) where f (x, y) and g(x, y) are the original and degraded images, respectively, and n(x, y) is the additive noise component of the degraded image.

25). Once the optimal value of the λ is found, an iterative algorithm which operates on one pixel at a time and constrains each pixel to be positive during each iteration will implicitly calculate the µ j . 5 Neural Network Restoration Neural network restoration approaches are designed to minimize a quadratic programming problem [46,95–98]. The generalized Hopfield network can be applied to this case [35]. 3), W, b and c are functions of H, D, λ, and n, and other problem related constraints. In terms of a neural network energy function, the (i, j)th element of W corresponds to the interconnection 36 Adaptive Image Processing: A Computational Intelligence Perspective strength between neurons (pixels) i and j in the network.

3 both minimize the current pixel’s energy contribution; hence they must both produce the same results. In practice the authors have found that all three algorithms generally produce identical results, which suggests that for reasonable values of the parameter λ, only a single global minimum is present. Note that in the discussion so far, we have not made any assumptions regarding the nature of the weighting matrix, W, or the bias vector, b. W and b determine where the solution is in the solution space, but as long as they are constant during the restoration procedure the algorithm will still terminate after a finite number of iterations.

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