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 program fields to which genetic and evolutionary computation (GEC) options are utilized. even though increasingly more examples of such functions are available in literature, they're scattered, except a couple of exceptions, in complaints and journals devoted to extra basic subject matters. This ebook is the 1st try to supply a breathtaking view at the box, through describing purposes of such a lot mainstream GEC options to a variety of difficulties in snapshot processing and research. greater than 20 top researchers within the box have contributed to this e-book, protecting subject matters starting from low-level snapshot processing to high-level photograph research in complex laptop imaginative and prescient functions. even supposing the e-book is especially application-oriented, specific care has been given to introducing GEC tools, in every one bankruptcy, at a degree which makes them available to a large viewers. the predicted aim of the ebook contains practitioners and researchers in picture research and processing who will not be conversant in GEC innovations. even as, the ebook can to boot be of curiosity for researchers in evolutionary computation, considering the fact that such a lot contributions concentrate on purposes of genetic and evolutionary ideas that are in response to nontrivial implementations of such equipment. this selection displays the character of the contributions that are authored either by way of researchers for which GEC is the most box of curiosity and via researchers whose paintings is principally interested by snapshot processing and research.

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3, pp. 657–662, Tokyo, Japan, October 1999. [34] S. J. P. Todd and W. Latham, Evolutionary Art and Computers, Academic Press, Amsterdam, The Netherlands, 1992. [35] M. edu/ga/. 1. Introduction Applications which require real-time or quasi-real-time processing are more and more frequent in computer vision, as well as in many other application fields, thanks to the continuous increase of computing power available to programmers. However, at the same time, such an increase keeps being challenged by the quantity of data on which applications must be run, which is increasing with a similar, when not faster, trend.

Collet, E. Lutton, M. Schoenauer, and J. Louchet, “Take it EASEA,” in Proceedings of the 6th International Conference on Parallel Problem Solving from Nature (PPSN ’00), vol. 1917, pp. 891– 901, Springer, Paris, France, September 2000. [6] K. Daoudi, J. L´evy V´ehel, and Y. Meyer, Construction of Functions with Prescribed Local Regularity, Constructive Approximation, Nashville, Tenn, USA, 1989. [7] R. A. DeVore and B. Lucier, “Fast wavelet techniques for near-optimal image processing,” in Proceedings of IEEE Military Communications Conference (MILCOM ’92), vol.

Setting a user preferred range for individual genes is done by drag and drop of the thick vertical brackets. Various selection algorithms have been implemented. These selection operators can be deployed by the parent, offspring, and image selection. The selection operator that is actually used in a certain stage of the genetic cycle is set offline with help of a configuration file. The available selection methods are the following. (i) “Fittest”: the individual with the best fitness value is selected.

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