Applications of Evolutionary Computation in Image Processing and Pattern Recognition / / by Erik Cuevas, Daniel Zaldívar, Marco Perez-Cisneros

• Record Nr.: UNINA9910254210003321
• Autore: Cuevas Erik
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
• ISBN: 3-319-26462-1
• Edizione: [1st ed. 2016.]
• Descrizione fisica: 1 online resource (284 p.)
• Collana: Intelligent Systems Reference Library, , 1868-4394 ; ; 100
• Disciplina: 006.3823
• Soggetti: Computational intelligence; Artificial intelligence; Signal processing; Image processing; Speech processing systems; Optical data processing; Calculus of variations; Computational Intelligence; Artificial Intelligence; Signal, Image and Speech Processing; Image Processing and Computer Vision; Calculus of Variations and Optimal Control; Optimization
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references at the end of each chapters.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.