Hybrid Soft Computing for Image Segmentation (Paperback, Softcover reprint of the original 1st ed. 2016)


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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Product Description

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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Product Details

General

Imprint

Springer International Publishing AG

Country of origin

Switzerland

Release date

June 2018

Availability

Expected to ship within 10 - 15 working days

First published

2016

Editors

, , ,

Dimensions

235 x 155 x 18mm (L x W x T)

Format

Paperback

Pages

321

Edition

Softcover reprint of the original 1st ed. 2016

ISBN-13

978-3-319-83684-3

Barcode

9783319836843

Categories

LSN

3-319-83684-6



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