By Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau San Wong
Illustrating crucial points of adaptive photograph processing from a computational intelligence point of view, the second one version of Adaptive picture Processing: A Computational Intelligence viewpoint offers an authoritative and unique account of computational intelligence (CI) equipment and algorithms for adaptive picture processing in regularization, aspect detection, and early vision.
With 3 new chapters and up-to-date details all through, the hot version of this renowned reference contains immense new fabric that specializes in purposes of complicated CI recommendations in picture processing purposes. It introduces new ideas and frameworks that exhibit how neural networks, aid vector machines, fuzzy common sense, and evolutionary algorithms can be utilized to deal with new demanding situations in picture processing, together with low-level picture processing, visible content material research, characteristic extraction, and development recognition.
Emphasizing advancements in cutting-edge CI innovations, comparable to content-based picture retrieval, this booklet maintains to supply educators, scholars, researchers, engineers, and technical managers in visible info processing with the up to date knowing required to deal with modern demanding situations in photograph content material processing and research.
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Additional info for Adaptive Image Processing: A Computational Intelligence Perspective (2nd Edition)
This approach is then further extended to solve the problem of blind deconvolution by including the point spread function coefficients in the set of hyperparameters associated with each individual in the population. 5 Application of Self-Organization to Image Analysis and Retrieval A recent approach known as the self-organizing tree map (SOTM)  inherently incorporates hierarchical properties by virtue of its growth, in a manner that is far more flexible in terms of revealing the underlying data space without being constrained by an imposed topological framework.
2) will produce a different g. It can be shown that given a fixed f, g has the following distribution: g|f : N(Hf, R) This is also called the conditional probability density of g given f and is denoted as p(g|f). 5), then we have found the maximum likelihood solution for f. The maximum likelihood solution is the value of f that maximizes the probability of obtaining our observed g. 5), we note that the denominator is not a function of f, so we can ignore it. 5) without affecting the solution, since the logarithm operator is a monotonic function and hence maxu (ln( f (u))) = maxu ( f (u)).
Blurring can be caused when an object in the image is outside the camera’s depth of field some time during the exposure. For example, a foreground tree might be blurred when we have set up a camera with a telephoto lens to take a photograph of a distant mountain. A blurred object loses some small-scale detail and the blurring process can be modeled as if high-frequency components have been attenuated in some manner in the image [4,21]. If an imaging system internally attenuates the high-frequency components in the image, the result will again appear blurry, despite the fact that all objects in the image were in the camera’s field of view.