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Computational Framework for Family of Order Statistic Filters for Tensor Valued Data

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Image Analysis and Recognition (ICIAR 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4141))

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Abstract

Nonlinear digital filters based on the order statistic belong to the very powerful methods of image restoration. The well known is the median filter operating on scalar valued images. However, the operation of the median filter can be extended into multi-valued pixels, such as colour images. It appears that we can go even further and define such filters for tensor valued data. Such a median filter for tensor valued images was originally proposed by Welk et.al. [10]. In this paper we present a different approach to this concept: We propose the family of nonlinear order statistic filters operating on tensor valued data and provide the computational framework for their non-numerical implementation.

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Author information

Authors and Affiliations

  1. AGH – University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Bogusław Cyganek

Authors
  1. Bogusław Cyganek
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Editors and Affiliations

  1. Departamento de Engenharia Electrotécnica e de Computadores da, Faculdade de Engenharia da, Universidade do Porto, Campus FEUP, 4200-465, Porto, Portugal

    Aurélio Campilho

  2. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed S. Kamel

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© 2006 Springer-Verlag Berlin Heidelberg

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Cyganek, B. (2006). Computational Framework for Family of Order Statistic Filters for Tensor Valued Data. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_15

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  • DOI: https://doi.org/10.1007/11867586_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44891-4

  • Online ISBN: 978-3-540-44893-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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