By Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau San Wong
Illustrating crucial points of adaptive photo processing from a computational intelligence point of view, the second one variation of Adaptive photograph Processing: A Computational Intelligence standpoint presents an authoritative and distinctive account of computational intelligence (CI) equipment and algorithms for adaptive photo processing in regularization, aspect detection, and early vision.
With 3 new chapters and up to date details all through, the recent version of this renowned reference contains huge new fabric that specializes in purposes of complex CI innovations in photograph processing purposes. It introduces new strategies and frameworks that reveal 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 photo processing, visible content material research, characteristic extraction, and development recognition.
Emphasizing advancements in state of the art CI suggestions, corresponding to content-based photo retrieval, this publication keeps to supply educators, scholars, researchers, engineers, and technical managers in visible details processing with the updated knowing required to deal with modern demanding situations in photo content material processing and research.
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Extra resources for Adaptive Image Processing: A Computational Intelligence Perspective (2nd Edition)
If h(x, y; α, β) has a simple form of space variance then H may have a simple form, resembling a block-Toeplitz matrix. 3 29 Image Restoration When an image is recorded suffering some type of degradation, such as mentioned above, it may not always be possible to take another, undistorted, image of the interesting phenomenon or object. The situation may not recur, like the image of a planet taken by a space probe, or the image of a crime in progress. On the other hand, the imaging system used may introduce inherent distortions to the image which cannot be avoided, for example, a magnetic resonance imaging system.
Fundamentals of CI-Inspired Adaptive Image Restoration 27 All linear image degradations can be described by their impulse response. A two-dimensional impulse response is often called a point spread function. It is a two-dimensional function that smears a pixel at its center with some of the pixel’s neighbors. The size and shape of the neighborhood used by the PSF is called the PSF’s region of support. Unless explicitly stated, we will from now on consider PSFs with square-shaped neighborhoods. The larger the neighborhood, the more smearing occurs and the worse the degradation to the image.
The constrained least square error measure can be viewed from different perspectives, and let’s explore two such viewpoints before going on to the neural network models. 1 A Bayesian Perspective The restoration problem may be considered in terms of probability distributions and Bayesian theory. 2) g = Hf + n Consider the case where n is a normally distributed random variable with mean 0 and a covariance matrix given by R. 4) where det R is the determinate of R, which is assumed to be invertible.