Hybrid Soft Computing for Image Segmentation
Editors: Bhattacharyya, S., Dutta, P., De, S., Klepac, G. (Eds.)
- 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
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- About this book
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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.
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- Download Sample pages 2 PDF (1.3 MB)
- Download Table of contents PDF (53.9 KB)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Hybrid Soft Computing for Image Segmentation
- Editors
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- Siddhartha Bhattacharyya
- Paramartha Dutta
- Sourav De
- Goran Klepac
- Copyright
- 2016
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing AG
- eBook ISBN
- 978-3-319-47223-2
- DOI
- 10.1007/978-3-319-47223-2
- Hardcover ISBN
- 978-3-319-47222-5
- Edition Number
- 1
- Number of Pages
- XVI, 321
- Number of Illustrations and Tables
- 75 b/w illustrations, 87 illustrations in colour
- Topics
