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Feature Thresholding in Generalized Approximation Spaces

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Man–Machine Interactions 4

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 391))

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Abstract

Recent advances in information sciences are extending classical set theory frontiers into new domains of uncertainty perception, incompleteness, vagueness of knowledge—giving new mathematical approach to development of intelligent information systems. The paper addresses the problem of construction of rough measures in generalized approximation spaces introducing a new method of rough feature thresholding. The algorithm creates rough feature blocks and assigns them image blocks from the block min, avg, max statistics. The algorithm converts data blocks into rough approximations of feature blocks. The introduced solution contributes to the highly precise internal data structure descriptors on one side and constitutes the algorithmic base for rough data analysis entirely embedded in generalized approximation spaces at the same time. The scope of possible applications includes image descriptors, image thresholding, image classifications.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland

    Dariusz Małyszko

Authors
  1. Dariusz Małyszko
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Correspondence to Dariusz Małyszko .

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Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Aleksandra Gruca

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Agnieszka Brachman

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

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© 2016 Springer International Publishing Switzerland

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Małyszko, D. (2016). Feature Thresholding in Generalized Approximation Spaces. In: Gruca, A., Brachman, A., Kozielski, S., Czachórski, T. (eds) Man–Machine Interactions 4. Advances in Intelligent Systems and Computing, vol 391. Springer, Cham. https://doi.org/10.1007/978-3-319-23437-3_44

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  • DOI: https://doi.org/10.1007/978-3-319-23437-3_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23436-6

  • Online ISBN: 978-3-319-23437-3

  • eBook Packages: EngineeringEngineering (R0)

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