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Fusion of Edge Information in Markov Random Fields Region Growing Image Segmentation

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Image Analysis and Recognition (ICIAR 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6111))

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Abstract

This paper proposes an algorithm that fuses edge information into Markov Random Fields (MRF) region growing based image segmentation. The idea is to segment the image in a way that takes edge information into consideration. This is achieved by modifying the energy function minimization process so that it would penalize merging regions that have real edges in the boundary between them. Experimental results confirming the hypothesis that the addition of edge information increases the precision of the segmentation by ensuring the conservation of the objects contours during the region growing.

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Author information

Authors and Affiliations

  1. School of Computing, University of Southern Mississippi, 118 College Drive Box#5106, Hattiesburg, MS, 39406, USA

    Amer Dawoud & Anton Netchaev

Authors
  1. Amer Dawoud
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  2. Anton Netchaev
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Editor information

Editors and Affiliations

  1. Faculty of Engineering, Institute of Biomedical Engineering, Rua Dr. Roberto Frias, University of Porto, 4200-465, Porto, Portugal

    Aurélio Campilho

  2. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada

    Mohamed Kamel

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Dawoud, A., Netchaev, A. (2010). Fusion of Edge Information in Markov Random Fields Region Growing Image Segmentation. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2010. Lecture Notes in Computer Science, vol 6111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13772-3_11

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  • DOI: https://doi.org/10.1007/978-3-642-13772-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13771-6

  • Online ISBN: 978-3-642-13772-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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