Hybrid Soft Computing for Image Segmentation

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 r...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Bhattacharyya, Siddhartha (Editor), Dutta, Paramartha (Editor), De, Sourav (Editor), Klepac, Goran (Editor)
Format: Electronic eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2016.
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Online Access:Full Text via HEAL-Link
Description
Summary: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.
Physical Description:XVI, 321 p. 162 illus., 87 illus. in color. online resource.
ISBN:9783319472232