Applications of Evolutionary Computation in Image Processing and Pattern Recognition〈1st ed. 2016〉

著者名：Cuevas, Erik/Zaldívar, Daniel/Perez-Cisneros, Marco

Springer（2015/11/07発売）

• 言語：ENG
• ISBN：9783319264608
• eISBN：9783319264622

Description

This book presents the use of efficientEvolutionary Computation (EC) algorithms for solving diverse real-world imageprocessing and pattern recognition problems. It provides an overview of thedifferent aspects of evolutionary methods in order to enable the reader inreaching a global understanding of the field and, in conducting studies onspecific evolutionary techniques that are related to applications in imageprocessing and pattern recognition. It explains the basic ideas of the proposedapplications in a way that can also be understood by readers outside of thefield. Image processing and pattern recognition practitioners who are notevolutionary computation researchers will appreciate the discussed techniquesbeyond simple theoretical tools since they have been adapted to solvesignificant problems that commonly arise on such areas. On the other hand,members of the evolutionary computation community can learn the way in whichimage processing and pattern recognition problems can be translated into anoptimization task. The book has been structured so that each chapter can beread independently from the others. It can serve as reference book for studentsand researchers with basic knowledge in image processing and EC methods.

Table of Contents

Introduction.- ImageSegmentation Based on Differential Evolution Optimization.-Motion EstimationBased on Artificial Bee Colony (ABC).- Ellipse Detection on ImagesInspired by the Collective Animal Behavior.- Template Matching by Usingthe States of Matter Algorithm.- Estimation of Multiple View RelationsConsidering Evolutionary Approaches.- Circle Detection on Images Based onan Evolutionary Algorithm that Reduces theNumber of Function Evaluations.- Otsu and Kapur Segmentation Basedon Harmony Search Optimization.- Leukocyte Detection by UsingElectromagnetism-Like Optimization.- Automatic Segmentation by Using an AlgorithmBased on the Behavior of Locust Swarms.