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F-Transform

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Springer Handbook of Computational Intelligence

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Abstract

The theory of the F-transform is presented and discussed from the perspective of the latest developments and applications. Various fuzzy partitions are considered. The definition of the F-transform is given with respect to a generalized fuzzy partition, and the main properties of the F-transform are listed. The applications to image processing, namely image compression, fusion and edge detection, are discussed with sufficient technical details.

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Abbreviations

CA:

complete F-transform-based fusion algorithm

ESA:

enhanced simple algorithm

FTR:

F-transform image compression

MSE:

mean square error

PSNR:

peak signal-to-noise ratio

RMSE:

root-mean-square error

SA:

simple F-transform-based fusion algorithm

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Author information

Authors and Affiliations

  1. Inst. Research and Applications of Fuzzy Modeling, University of Ostrava, 30. dubna 22, 70103, Ostrava, Czech Republic

    Irina Perfilieva

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  1. Irina Perfilieva
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Correspondence to Irina Perfilieva .

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Editors and Affiliations

  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Perfilieva, I. (2015). F-Transform. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_7

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  • DOI: https://doi.org/10.1007/978-3-662-43505-2_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43504-5

  • Online ISBN: 978-3-662-43505-2

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