Read e-book online Applications of Evolutionary Computation in Image Processing PDF

By Erik Cuevas, Daniel Zaldívar, Marco Perez-Cisneros

ISBN-10: 3319264605

ISBN-13: 9783319264608

ISBN-10: 3319264621

ISBN-13: 9783319264622

This ebook offers using effective Evolutionary Computation (EC) algorithms for fixing different real-world picture processing and trend popularity difficulties. It presents an summary of the several facets of evolutionary equipment which will allow the reader in attaining a world figuring out of the sector and, in carrying out reviews on particular evolutionary innovations which are regarding purposes in photograph processing and trend reputation. It explains the elemental rules of the proposed purposes in a fashion that could even be understood by means of readers open air of the sphere. photo processing and trend popularity practitioners who're no longer evolutionary computation researchers will enjoy the mentioned strategies past basic theoretical instruments considering the fact that they've been tailored to unravel major difficulties that ordinarily come up on such components. nonetheless, participants of the evolutionary computation neighborhood can find out how during which picture processing and trend popularity difficulties could be translated into an optimization activity. The booklet has been dependent in order that every one bankruptcy should be learn independently from the others. it might probably function reference e-book for college kids and researchers with simple wisdom in photograph processing and EC methods.

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The parameters ðP; r; lÞ are randomly initialized, but assuming some restrictions to each parameter (for example l must fall between 0 and 255). 5. 5 Experimental Results 15 (a) (b) Fig. 1 a Original “The Cameraman” image, and b its correspondent histogram In theory, the search for optimal values using DE covers all the search space. However, some sets of parameters chosen within the search space might not provide plausible solutions to the problem. Restricting the search space to a feasible region might also be difficult because there are not simple constraints [22].

Such estimations are based on an approximate model of the fitness landscape. Thus, the individuals to be evaluated and those to be estimated are determined following some fixed criteria which depend on the specific properties of the approximate model [41]. The models involved at the estimation can be built during the actual EA run, since EA repeatedly samples the search space at different points [42]. g. polynomials [43], the kriging model [44], the feed-forward neural networks that includes multi-layer Perceptrons [45] and radial basis-function networks [46]).

The “fitness value” refers to the food source quality which is strongly linked to the food’s location. The process mimics the bee’s search for valuable food sources yielding an analogous process for finding the optimal solution. 1 Biological Bee Profile The minimal model for a honey bee colony consists of three classes: employed bees, onlooker bees and scout bees. The employed bees will be responsible for investigating about food sources and sharing the information to recruit onlooker bees, which in turn, will make a decision on choosing food sources considering such information.

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Applications of Evolutionary Computation in Image Processing and Pattern Recognition by Erik Cuevas, Daniel Zaldívar, Marco Perez-Cisneros


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