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Improved Edge Detection Algorithms Based on a Riesz Fractional Derivative

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Book cover Image Analysis and Recognition (ICIAR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10882))

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Abstract

In this paper we generalize some classical edge detectors using the second-order Riesz fractional derivative. Taking advantages of fractional differential method we improve the shortcomings of conventional operators like Roberts, Prewitt and Sobel. Consequently, three improved edge detection algorithms are gained. The experimental results show that the proposed models enhance edge information effectively and reveal more detailed information than traditional operators.

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References

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Author information

Authors and Affiliations

  1. Department of Mathematical Methods and Models, University Politehnica of Bucharest, Bucharest, Romania

    Carmina Georgescu

Authors
  1. Carmina Georgescu
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Correspondence to Carmina Georgescu .

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Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  3. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Bart ter Haar Romeny

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Georgescu, C. (2018). Improved Edge Detection Algorithms Based on a Riesz Fractional Derivative. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_23

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  • DOI: https://doi.org/10.1007/978-3-319-93000-8_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92999-6

  • Online ISBN: 978-3-319-93000-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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