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Image Processing and Computer Vision

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Fuzzy Models and Algorithms for Pattern Recognition and Image Processing

Part of the book series: The Handbooks of Fuzzy Sets Series ((FSHS,volume 4))

Abstract

Digital image processing is the study of theories, models and algorithms for the manipulation of images (usually by computer). It spans a wide variety of topics such as digitization, histogram manipulation, warping, filtering, segmentation, restoration and compression. Computer vision deals with theories and algorithms for automating the process of visual perception, and involves tasks such as noise removal, smoothing, and sharpening of edges (low-level vision); segmentation of images to isolate object regions, and description of the segmented regions (intermediate-level vision); and finally, interpretation of the scene (high-level vision). Thus, there is much overlap between these two fields. In this chapter, we concentrate on some of the aspects of image processing and computer vision in which a fuzzy approach has had an impact. We begin with some notation and definitions used throughout the chapter.

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Author information

Authors and Affiliations

  1. University of West Florida, USA

    James C. Bezdek

  2. University of Missouri, USA

    James Keller

  3. Colorado School of Mines, USA

    Raghu Krisnapuram

  4. Indian Statistical Institute, India

    Nikhil R. Pal

Authors
  1. James C. Bezdek
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  2. James Keller
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  3. Raghu Krisnapuram
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  4. Nikhil R. Pal
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© 1999 Springer Science+Business Media New York

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Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.R. (1999). Image Processing and Computer Vision. In: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. The Handbooks of Fuzzy Sets Series, vol 4. Springer, Boston, MA. https://doi.org/10.1007/0-387-24579-0_5

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  • DOI: https://doi.org/10.1007/0-387-24579-0_5

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-24515-7

  • Online ISBN: 978-0-387-24579-9

  • eBook Packages: Springer Book Archive

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