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A New Edge Detection Approach Based on Fuzzy Segments Clustering

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Book cover Advances in Fuzzy Logic and Technology 2017 (EUSFLAT 2017, IWIFSGN 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 642))

Abstract

Edge detection comprises different stages that go from adaptation of the original image -conditioning- to the selection of the definitive edges. This last step, known as scaling, requires the application of a thresholding process over the gradients of luminosity values of the pixels. Traditionally, this is made through a local evaluation process that works pixel by pixel. As the edge candidate pixels are not independent, a wider strategy suggests the use of a more global evaluation. In this sense, this approach resembles more the human vision. This paper further develops ideas related to edge lists, first proposed in 1995 [1]. This paper will refer to edge lists as edge segments. These segments contain visual features similar to the ones that humans use, which might lead to better comparative results. In this paper we propose using clustering techniques to differentiate the appropriate segments or true segments from the false ones, and we introduce an algorithm that uses fuzzy clustering techniques. Finally, this paper shows that this fuzzy clustering over the segments performs at least as well as other standard algorithms used for edge detection.

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Acknowledgement

It has been strongly helpfull for the conducting of this research the code of KERMIT Research Unit (Ghent University), The Kermit Image Toolkit (KITT), B. De Baets, C. Lopez-Molina (Eds.), Available online at www.kermitimagetoolkit.net.

Author information

Authors and Affiliations

  1. Facultad de Estudios Estadísticos, Complutense University, 28040, Madrid, Spain

    Pablo A. Flores-Vidal & Daniel Gómez

  2. Facultad de Ciencias Económicas, Complutense University, 28223, Madrid, Spain

    Pablo Olaso

  3. Facultad de Ciencias Matemáticas, Complutense University, 28040, Madrid, Spain

    Carely Guada

Authors
  1. Pablo A. Flores-Vidal
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  2. Daniel Gómez
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  3. Pablo Olaso
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  4. Carely Guada
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Corresponding author

Correspondence to Pablo A. Flores-Vidal .

Editor information

Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Eulalia Szmidt

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Slawomir Zadrożny

  4. Department of Telecommunications and Information Processing, Bulgarian Academy of Sciences, Sofia, Bulgaria

    K. T. Atanassov

  5. WIT - Warsaw School of Information Technology, Warsaw, Poland

    Maciej Krawczak

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Flores-Vidal, P.A., Gómez, D., Olaso, P., Guada, C. (2018). A New Edge Detection Approach Based on Fuzzy Segments Clustering. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-319-66824-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-66824-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66823-9

  • Online ISBN: 978-3-319-66824-6

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