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Hybrid Soft Computing for Multilevel Image and Data Segmentation (Computational Intelligence Methods and Applications)

Hybrid Soft Computing for Multilevel Image and Data Segmentation (Computational Intelligence Methods and Applications)

Hybrid Soft Computing for Multilevel Image and Data Segmentation (Computational
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Hybrid Soft Computing for Multilevel Image and Data Segmentation (Computational Intelligence Methods and Applications) Hardback - 2016

by Bhattacharyya, Siddhartha

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hardcover. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
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Details

  • Title Hybrid Soft Computing for Multilevel Image and Data Segmentation (Computational Intelligence Methods and Applications)
  • Author Bhattacharyya, Siddhartha
  • Binding Hardback
  • Condition Used - Good
  • Pages 235
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date 2016-11-18
  • Illustrated Yes
  • Features Illustrated
  • Bookseller's Inventory # 3319475231.G
  • ISBN 9783319475233 / 3319475231
  • Weight 1.17 lbs (0.53 kg)
  • Dimensions 9.21 x 6.14 x 0.63 in (23.39 x 15.60 x 1.60 cm)
  • Category Computers - General Information
  • Dewey Decimal Code 006.3
  • Quantity available 1

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Reader reviews for Hybrid Soft Computing for Multilevel Image and Data Segmentation (Computational Intelligence Methods and Applications)

From the publisher

This book explains efficient solutions for segmenting the intensity levels of different types of multilevel images. The authors present hybrid soft computing techniques, which have advantages over conventional soft computing solutions as they incorporate data heterogeneity into the clustering/segmentation procedures.

This is a useful introduction and reference for researchers and graduate students of computer science and electronics engineering, particularly in the domains of image processing and computational intelligence.

From the rear cover

This book explains efficient solutions for segmenting the intensity levels of different types of multilevel images. The authors present hybrid soft computing techniques, which have advantages over conventional soft computing solutions as they incorporate data heterogeneity into the clustering/segmentation procedures.

This is a useful introduction and reference for researchers and graduate students of computer science and electronics engineering, particularly in the domains of image processing and computational intelligence.

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