Full Description
This book presents the use of efficient
Evolutionary Computation (EC) algorithms for solving diverse real-world image
processing and pattern recognition problems. It provides an overview of the
different aspects of evolutionary methods in order to enable the reader in
reaching a global understanding of the field and, in conducting studies on
specific evolutionary techniques that are related to applications in image
processing and pattern recognition. It explains the basic ideas of the proposed
applications in a way that can also be understood by readers outside of the
field. Image processing and pattern recognition practitioners who are not
evolutionary computation researchers will appreciate the discussed techniques
beyond simple theoretical tools since they have been adapted to solve
significant problems that commonly arise on such areas. On the other hand,
members of the evolutionary computation community can learn the way in which
image processing and pattern recognition problems can be translated into an
optimization task. The book has been structured so that each chapter can be
read independently from the others. It can serve as reference book for students
and researchers with basic knowledge in image processing and EC methods.
Contents
Introduction.- Image
Segmentation Based on Differential Evolution Optimization.-Motion Estimation
Based on Artificial Bee Colony (ABC).- Ellipse Detection on Images
Inspired by the Collective Animal Behavior.- Template Matching by Using
the States of Matter Algorithm.- Estimation of Multiple View Relations
Considering Evolutionary Approaches.- Circle Detection on Images Based on
an Evolutionary Algorithm that Reduces the
Number of Function Evaluations.- Otsu and Kapur Segmentation Based
on Harmony Search Optimization.- Leukocyte Detection by Using
Electromagnetism-Like Optimization.- Automatic Segmentation by Using an Algorithm
Based on the Behavior of Locust Swarms.
