Evolutionary Image Analysis and Signal Processing
Springer Science & Business Media, Jun 22, 2009 - Mathematics - 204 pages
The publication of this book on evolutionaryImage Analysis and Signal P- cessing (IASP) has two main goals. The ?rst, occasional one is to celebrate the 10th edition of EvoIASP, the workshop which has been the only event speci?cally dedicated to this topic since 1999. The second, more important one is to give an overview of the opportunities o?ered by Evolutionary C- putation (EC) techniques to computer vision,pattern recognition,and image and signal processing. It is not possible to celebrate EvoIASP properly without ?rst ackno- edging EvoNET, the EU-funded network of excellence, which has made it possible for Europe to build a strong European research community on EC. Thanks to the success of the ?rst, pioneering event organized by EvoNET, held in 1998 in Paris, it was possible to realize that not only was EC a f- tile ground for basic research but also there were several application ?elds to which EC techniques could o?er a valuable contribution. That was how the ideaofcreatingasingleevent,EvoWorkshops,outofacollectionofworkshops dedicated to applications of EC, was born. Amongst the possible application ?elds for EC, IASP was selected almost accidentally, due to the occasional presence, within EvoNET, of less than a handful of researchers who were interested in it. I would lie if I stated that the event was a great success since its very start, but it was successful enough to survive healthily for a couple of years, before reaching its present size, relevance, and popularity.
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afﬁne applications Artiﬁcial Cagnoni classiﬁcation problems coefficients Computer Vision corresponding data sets deﬁned deﬁnition detection detectors Differential Evolution difﬁcult distance ellipse error evaluation Evolution Strategies evolutionary algorithm Evolutionary Computation evolved ﬁelds ﬁlters ﬁnal ﬁnd FIR filter ﬁrst ﬁtness function ﬁtness value ﬁtting FPGA FU row genetic algorithm genetic programming GLASM GLCM GP approach Heidelberg I-EvoSeg IEEE Transactions image processing image registration image segmentation implemented individual input image integer interactive interest point detection IVUS KIPGP1 learner LNCS medical image method MIES modi nodes modi rate MPT program multiclass object classiﬁcation mutation obtained Olague operators optimization output parabola parameters Pattern Recognition performance pixels population program structure proposed recognition system regions robot scale space scale-invariant scan matching search space selected solution speciﬁc Springer step-size success ratio task techniques texture descriptors tion transformation variables vector visual