Overview
- Topic important in applications such as image processing, image mining, video surveillance, and intelligent transportation systems
- Valuable for researchers and graduate students in the domains of image processing and computational intelligence
- Contributions examine strengths and weaknesses of the approaches
- Includes supplementary material: sn.pub/extras
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Table of contents (12 chapters)
Keywords
About this book
This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems. The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty; artificial neural networks, usually applied for machine cognition, learning, and recognition; and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization.
The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.
Editors and Affiliations
Bibliographic Information
Book Title: Hybrid Soft Computing for Image Segmentation
Editors: Siddhartha Bhattacharyya, Paramartha Dutta, Sourav De, Goran Klepac
DOI: https://doi.org/10.1007/978-3-319-47223-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2016
Hardcover ISBN: 978-3-319-47222-5Published: 18 November 2016
Softcover ISBN: 978-3-319-83684-3Published: 29 June 2018
eBook ISBN: 978-3-319-47223-2Published: 12 November 2016
Edition Number: 1
Number of Pages: XVI, 321
Number of Illustrations: 75 b/w illustrations, 87 illustrations in colour
Topics: Artificial Intelligence, Computational Intelligence, Computer Imaging, Vision, Pattern Recognition and Graphics