Genetic and Evolutionary Computation for Image Processing by Edited by: Stefano Cagnon, Evelyne Lutto, and Gustavo Olagu

By Edited by: Stefano Cagnon, Evelyne Lutto, and Gustavo Olagu

Picture research and processing is gradually gaining relevance in the huge variety of software fields to which genetic and evolutionary computation (GEC) options are utilized. even if progressively more examples of such purposes are available in literature, they're scattered, except a couple of exceptions, in complaints and journals devoted to extra normal themes. This publication is the 1st try to provide a wide ranging view at the box, by means of describing functions of such a lot mainstream GEC recommendations to quite a lot of difficulties in photo processing and research. greater than 20 best researchers within the box have contributed to this ebook, masking issues starting from low-level photo processing to high-level photo research in complicated desktop imaginative and prescient purposes. even though the publication is principally application-oriented, specific care has been given to introducing GEC tools, in each one bankruptcy, at a degree which makes them obtainable to a large viewers. the anticipated aim of the booklet includes practitioners and researchers in picture research and processing who will not be conversant in GEC recommendations. whilst, the publication can to boot be of curiosity for researchers in evolutionary computation, due to the fact that such a lot contributions concentrate on functions of genetic and evolutionary innovations that are in line with nontrivial implementations of such equipment. this option displays the character of the contributions that are authored either through researchers for which GEC is the most box of curiosity and through researchers whose paintings is especially eager about picture processing and research.

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Extra resources for Genetic and Evolutionary Computation for Image Processing and Analysis (EURASIP Book Series on Signal Processing and Communications)

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Fitness and user interaction. 8 with range [−6 (very bad), . . , 0 (neutral), . . + 6 (very good)]. The genetic engine is highly customizable for parameters setting. In contrast to the small population IEA, it is possible that all 6 images in the user interface are changed from a generation to the next. As loosing good images may be frustrating for the user, it is possible to mark images as “super individuals” that remain in the user interface and in the population. 6. 5. The extended genetic engine supports a fitness map.

The use of wavelets then allows to perform the reconstruction in a simple way. Starting from the coefficient (d j,k ) of the observations, we will define a procedure that modifies them to obtain coefficients (c j,k ) that fulfil the decay condition with the desired α, and then reconstruct X from those (c j,k ). We may now reformulate our problem as follows: for a given set of observations Y = (Y1 , . . , Y2n ) and a target H¨older function α , find X such that X − Y L2 is minimum and the regression of the logarithm of the wavelet coefficients of X above any point i with respect to scale is −(α(i) + 1/2).

One says that f ∈ Clα (Ω) if ∃ C : ∀x, y ∈ Ω : f (x) − f (y) ≤ C. 1) Let αl ( f , x0 , ρ) = sup{α : f ∈ Clα (B(x0 , ρ))}. Note that αl ( f , x0 , ρ) is nonincreasing as a function of ρ. We are now in position to give the definition of the local H¨older exponent. 1. Let f be a continuous function. The local H¨older exponent of f at x0 is the number αl ( f , x0 ) = limρ→0 αl ( f , x0 , ρ). Since αl is defined at each point, we may associate to f the function x → αl (x) which measures the evolution of its regularity.

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