Applications of Evolutionary Computation in Image Processing and Pattern Recognition: 100 (Intelligent Systems Reference Library, 100) - Softcover

Cuevas, Erik; Zaldívar, Daniel; Perez-Cisneros, Marco

Softcover

ISBN 10: 3319370995 ISBN 13: 9783319370996

Publisher: Springer, 2016

Synopsis

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.

About this title

About the Author

Dr. 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. 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. Daniel Zaldivar graduated from the University of Guadalajara, Mexico in 1995 with a B.S. degree in Electronics and Communications Engineering. Later, in 2000, he earned his M.Sc. degree in Industrial Electronics from ITESO, Mexico, and in 2006 he received his Ph.D. degree from Freie Universität Berlin, Germany. Since then, he has been employed as a full-time Professor in the Department of Computer Science at the University of Guadalajara, where he currently holds his position. Ernesto Ayala, originally from León, Guanajuato was born in 1982. He received the title of Electrical Mechanical Engineer in 2017 and in 2019 the master's degree in Applied Computing at the University of Guadalajara. He is currently a PhD candidate in Electronics and Computing Sciences. Since 2018, he has been teaching curricular courses in Robotics Engineering and Electronic Engineering in the Division of Technologies for Cyber-human Integration of the University Center for Exact Sciences and Engineering. His area of expertise is computer vision and evolutionary computing. Mr. Ayala collaborates with a research group atthe University of Guadalajara focused on the development of ecological and autonomous driving vehicles. Oscar González received his B.S. with distinction in Electronic Engineering and Communications from the University of Guadalajara, Mexico, in 2022. During the COVID-19 pandemic, he was a member of the advisory committee for the COVID-19 pandemic of the University of Guadalajara. For his contributions and studies on COVID-19, he has been awarded the Irene Robledo García Award, the highest distinction of the University of Guadalajara for social service in 2022. Fernando Vega received the title of technical career in electricity by C.B.E.T.I.S. in 2014. Obtained a B.S. degree in Mechatronics from the National Technologist of Mexico, campus Culiacan, Mexico, in 2019. He is part of the University of Guadalajara, a full-time student M.S. in the Electronics and Computer Science program. His current research interests include motors design, electric vehicle design, Metaheuristics.

From the Back Cover

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.

Edition details

• Publisher: Springer
• Publication date: 2016
• Language: English
• ISBN 10: 3319370995
• ISBN 13: 9783319370996
• Binding: Paperback
• Number of pages: 274