Hybrid soft computing for image segmentation /

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Bibliographic Details
Imprint:Cham, Switzerland : Springer, 2016.
Description:1 online resource
Subject:Image segmentation.
Soft computing.
Artificial intelligence.
Computer vision.
COMPUTERS -- General.
Image segmentation.
Soft computing.
Electronic books.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11269051
Hidden Bibliographic Details
Other authors / contributors:Bhattacharyya, Siddhartha, 1975- editor.
Dutta, Paramartha, editor.
De, Sourav, 1979- editor.
Klepac, Goran, 1972- editor.
Digital file characteristics:text file PDF
Notes:Includes index.
Includes bibliographical references at the end of each chapters and index.
Online resource; title from PDF title page (SpringerLink, viewed November 30, 2016).
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.
Other form:Printed edition: 9783319472225
Standard no.:10.1007/978-3-319-47223-2