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Soft Computing for Image Processing

  • Sankar K. Pal
  • Ashish Ghosh
  • Malay K. Kundu

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 42)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Soft Computing and Image Analysis: Features, Relevance and Hybridization

    1. Sankar K. Pal, Ashish Ghosh, Malay K. Kundu
      Pages 1-20
  3. Preprocessing and Feature Extraction

    1. Front Matter
      Pages 21-21
    2. Todd Law, Koji Yamada, Daisuke Shibata, Tsuyoshi Nakamura, Lifeng He, Hidenori Itoh
      Pages 44-78
    3. Suman K. Mitra, C. A. Murthy, M. K. Kundu
      Pages 79-100
    4. Sunanda Mitra, Ramiro Castellanos, Su-Yu Yang, Surya Pemmaraju
      Pages 101-129
    5. B. Uma Shankar, A. Ghosh, S. K. Pal
      Pages 130-161
    6. Wladyslaw Skarbek
      Pages 162-182
    7. Lifeng He, Yuyan Chao, Tsuyoshi Nakamura, Hidenori Itoh
      Pages 183-204
    8. Vito Di Gesu
      Pages 246-259
    9. Joon H. Han, Tae Y. Kim, László T. Kóczy
      Pages 260-295
  4. Classification

    1. Front Matter
      Pages 297-297
    2. Nikola K. Kasabov, Brendon J. Woodford, Steven I. Israel
      Pages 318-336
    3. Kurt D. Bollacker, Joydeep Ghosh
      Pages 375-400
    4. K. Chidananda Gowda, H. N. Srikanta Prakash, P. Nagabhushan
      Pages 401-428
  5. Applications

    1. Front Matter
      Pages 429-429
    2. Nasser M. Nasrabadi, Sandor Der, Lin-Cheng Wang, Syed Rizvi, Alex Chan
      Pages 431-472
    3. Srinivas Guttat, Harry Wechsler
      Pages 473-506
    4. V. Susheela Devi, M. Narasimha Murty
      Pages 507-524
    5. Terry L. Huntsberger, John R. Rose, Dudley Girard
      Pages 525-551
    6. Hyun Mun Kim, Bart Kosko
      Pages 552-582
  6. Back Matter
    Pages 583-591

About this book

Introduction

Any task that involves decision-making can benefit from soft computing techniques which allow premature decisions to be deferred. The processing and analysis of images is no exception to this rule. In the classical image analysis paradigm, the first step is nearly always some sort of segmentation process in which the image is divided into (hopefully, meaningful) parts. It was pointed out nearly 30 years ago by Prewitt (1] that the decisions involved in image segmentation could be postponed by regarding the image parts as fuzzy, rather than crisp, subsets of the image. It was also realized very early that many basic properties of and operations on image subsets could be extended to fuzzy subsets; for example, the classic paper on fuzzy sets by Zadeh [2] discussed the "set algebra" of fuzzy sets (using sup for union and inf for intersection), and extended the defmition of convexity to fuzzy sets. These and similar ideas allowed many of the methods of image analysis to be generalized to fuzzy image parts. For are cent review on geometric description of fuzzy sets see, e. g. , [3]. Fuzzy methods are also valuable in image processing and coding, where learning processes can be important in choosing the parameters of filters, quantizers, etc.

Keywords

algorithm algorithms artificial neural network classification cognition filtering fuzzy fuzzy logic genetic algorithms image processing learning motion estimation networks neural networks pattern recognition

Editors and affiliations

  • Sankar K. Pal
    • 1
  • Ashish Ghosh
    • 1
  • Malay K. Kundu
    • 1
  1. 1.Machine Intelligence UnitIndian Statistical InstituteCalcuttaIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-7908-1858-1
  • Copyright Information Physica-Verlag Heidelberg 2000
  • Publisher Name Physica, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-7908-2468-1
  • Online ISBN 978-3-7908-1858-1
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site