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Introduction

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Fuzzy Sets Methods in Image Processing and Understanding

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Abstract

Since its introduction (Zadeh, Inf Control 8:338–353, 1965), fuzzy sets theory was rapidly exploited and further developed for image processing and image understanding problems. One reason for this development is that fuzzy sets provide a consistent mathematical framework for dealing with imprecision in knowledge representation, information modeling at different levels, fusion of heterogeneous information, reasoning, and decision making. In this book, we present the main research works on fuzzy techniques for image understanding, with emphasis on recent ones and emerging topics. It includes several works not only by the authors but also by many other ones. It is assumed that the reader is familiar with basics in image processing, analysis, and understanding (that can be found, e.g., in Maître (Image Processing. ISTE Wiley, London, UK, 2008)), although the book is rather self-contained. Applicative examples illustrate each addressed topic, with emphasis on medical imaging.

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Notes

  1. 1.

    http://graphicwg.irafm.osu.cz/index.php/Main_Page.

  2. 2.

    http://www.fuzzy.ugent.be/SCIP/index.html.

  3. 3.

    http://www.eusflat.org/.

  4. 4.

    http://www.itk.org.

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Authors and Affiliations

  1. Sorbonne Université, CNRS, LIP6, Paris, France

    Isabelle Bloch

  2. LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France

    Isabelle Bloch

  3. Department of Computer Science, University of Cincinnati, ML 0030, Cincinnati, OH, USA

    Anca Ralescu

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  1. Isabelle Bloch
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Bloch, I., Ralescu, A. (2023). Introduction. In: Fuzzy Sets Methods in Image Processing and Understanding. Springer, Cham. https://doi.org/10.1007/978-3-031-19425-2_1

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