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Computational Intelligence in Image Processing by Amitava Chatterjee, Patrick Siarry

By Amitava Chatterjee, Patrick Siarry

Computational intelligence established ideas have firmly confirmed themselves as manageable, exchange, mathematical instruments for greater than a decade. they've been greatly hired in lots of platforms and alertness domain names, between those sign processing, computerized regulate, business and shopper electronics, robotics, finance, production structures, electrical strength structures, and gear electronics. picture processing is additionally an incredibly effective quarter which has attracted the atten­tion of many researchers who're attracted to the improvement of recent computational intelligence-based suggestions and their appropriate purposes, in either learn prob­lems and in real-world difficulties.

Part I of the booklet discusses numerous snapshot preprocessing algorithms; half II largely covers photograph compression algorithms; half III demonstrates how computational intelligence-based suggestions should be successfully applied for photograph research reasons; and half IV indicates how development reputation, category and clustering-based concepts could be constructed for the aim of picture inferencing. The booklet deals a unified view of the trendy computational intelligence tech­niques required to resolve real-world difficulties and it truly is appropriate as a reference for engineers, researchers and graduate students.

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P. Ha, G. Fang and S. Y. Chen Abstract Contrast enhancement is a fundamental procedure in applications requiring image processing. Indeed, image enhancement contributes critically to the success of subsequent operations such as feature detection, pattern recognition and other higher-level processing tasks. Of interest among methods available for contrast enhancement is the intensity modification approach, which is based on the statistics of pixels in a given image. However, due to variations in the imaging condition and the nature of the scene being captured, it turns out that global manipulation of an image may be vulnerable to a noticeable quality degradation from distortion and noise.

We make two sets of assumptions regarding these processes: Immigration: The rate of immigration (λ) declines with the number of species (S) present on the habitat. Maximum immigration rate (I ) occurs when the habitat is empty and decreases as more species are added. Once all the potential colonists are on the habitat, then one can write S = Smax (the maximum number of species the habitat can support), and the immigration rate must be equal to zero. Generally speaking, 46 I. Boussaïd et al. Fig.

23) For notational brevity, we simply write n = Smax . 1. 3, where rand(0, 1) is a uniformly distributed random number in the interval [0, 1]; X i j is the jth SIV of the solution X i . The likelihood that a given solution S is expected a priori to exist as a solution for the given problem is indicated by the species count probability Ps . In this context it should be remarked that very high HSI solutions and very low HSI solutions are both equally improbable. Medium HSI solutions are relatively probable.

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