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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2015: Artificial Intelligence and Soft Computing pp 638–648Cite as

Interpretation of Image Segmentation in Terms of Justifiable Granularity

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9119))

Abstract

The principle of justifiable granularity, as formulated in [1], defines intuitively motivated requirements for an information granule to be meaningful. In the paper, granulation of images obtained by their segmentation is considered. In this context, such concepts as representation of granules and their relations, representation of concepts, consideration of context, detection and treatment of outliers, and recognition method, are of importance. The granular approach is related to intelligent analysis of all kinds of data, not only the computer images.

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Author information

Authors and Affiliations

  1. Institute of Information Technology, Lodz University of Technology, ul. Wólczańska 215, 90-924, Lodz, Poland

    Piotr S. Szczepaniak

Authors
  1. Piotr S. Szczepaniak
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Corresponding author

Correspondence to Piotr S. Szczepaniak .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafal Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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© 2015 Springer International Publishing Switzerland

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Szczepaniak, P.S. (2015). Interpretation of Image Segmentation in Terms of Justifiable Granularity. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_57

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  • DOI: https://doi.org/10.1007/978-3-319-19324-3_57

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19323-6

  • Online ISBN: 978-3-319-19324-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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