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Fuzzy Measures in Image Processing

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Fuzzy Sets and Their Extensions: Representation, Aggregation and Models

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 220))

Abstract

This chapter provides an overview of fuzzy measures and fuzzy integrals, measures of fuzziness, and their application in image processing in the areas of region based segmentation, thresholding, and color retreieval. This chapter also introduces a fuzzy color image retrieval method using a new type of membership function called beta membership function with Generalized Tversky’s index as a measure of fuzziness. Lastly, the proposed method has been compared with some existing fuzzy and non-fuzzy methods.

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Authors
  1. Tamalika Chaira
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Editor information

Editors and Affiliations

  1. Department of Automatics and Computation, Universidad Pública de Navarra, Campus Arrosadia s/n, 31006 Pamplona, Spain

    Humberto Bustince

  2. Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain

    Francisco Herrera

  3. Faculty of Mathematics, Universidad Complutense, Plaza de las Ciencias 3, 28040 Madrid, Spain

    Javier Montero

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© 2008 Springer-Verlag Berlin Heidelberg

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Chaira, T. (2008). Fuzzy Measures in Image Processing. In: Bustince, H., Herrera, F., Montero, J. (eds) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Studies in Fuzziness and Soft Computing, vol 220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73723-0_30

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  • DOI: https://doi.org/10.1007/978-3-540-73723-0_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73722-3

  • Online ISBN: 978-3-540-73723-0

  • eBook Packages: EngineeringEngineering (R0)

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