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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.
List 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.
About the author
Erik Cuevas received his B.S. degree with distinction in Electronics and Communications Engineering from the University of Guadalajara, Mexico, in 1995, the M.Sc. degree in Industrial Electronics from ITESO, Mexico, in 2000, and the Ph.D. degree from Freie Universität Berlin, Germany in 2006. Since 2006 he has been with the University of Guadalajara, where he is currently a full-time Professor in the Department of Computer Science. Since 2008, he is a member of the Mexican National Research System (SNI III). He is the author of several books and articles. A list of his books and publications can be seen in the CV attached to this application. His current research interest includes Meta-heuristics, computer vision, and mathematical methods. He serves as an editor in Expert System with Applications, ISA Transactions, and Applied Soft Computing, Applied Mathematical Modeling and Mathematics and Computers in Simulation.
Alberto Luque Chang graduated with a Bachelor's Degree in Communications and Electronics Engineering (2013), a Master of Science in Electronic Engineering and Computing (2016), and a Doctorate in Electronics and Computing Sciences (2021) in the University of Guadalajara (UdeG). He is currently a professor in the Division of Technologies for Cyber-Human Integration at the University Center for Exact Sciences and Engineering (CUCEI) of the UdeG. Likewise, since 2021, Dr. Luque is a member of the National System of Researchers, having the distinction of National Researcher Level 1. His areas of interest in research are Metaheuristic Algorithms, Artificial Intelligence, Optimization, Machine Learning and its applications. to Image Processing.
Héctor Escobar received a B.S. degree with honors in Information Systems Engineering from the Autonomous University of Sinaloa, Mexico, in 2018 and an M.S. degree in Electronics and Computer Engineering from the University of Guadalajara, Mexico, in 2021. He is part of the Universityof Guadalajara, where he is a full-time Ph.D. student in the Electronics and Computer Science program. His current research interests include Metaheuristics, computer vision, artificial intelligence, and Agent-Based Modeling.
Summary
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.
