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Title
Applications of evolutionary computation in image processing and pattern recognition / Erik Cuevas, Daniel Zaldívar, Marco Perez-Cisneros, editors.
ISBN
9783319264622 (electronic book)
3319264621 (electronic book)
9783319264608
Published
Cham : Springer, [2016]
Language
English
Description
1 online resource.
Other Standard Identifiers
10.1007/978-3-319-26462-2 doi
Call Number
TA347.E96
Dewey Decimal Classification
006.3823
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (viewed November 18, 2015).
Series
Intelligent systems reference library ; v. 100.
Available in Other Form
Print version: 9783319264608
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.
