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Adaptive Regularization Parameter for Graph Cut Segmentation

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Book cover Image Analysis and Recognition (ICIAR 2010)

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

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Abstract

Graph cut minimization formulates the segmentation problem as the liner combination of data and smoothness terms. The smoothness term is included in the energy formulation through a regularization parameter. We propose that the trade-off between the data and the smoothness terms should not be balanced by the same regularization parameter for the whole image. In order to validate the proposed idea, we build a system which adaptively changes the effect of the regularization parameter for the graph cut segmentation. The method calculates the probability of being part of the boundary for each pixel using the Canny edge detector at different hysteresis threshold levels. Then, it adjusts the regularization parameter of the pixel depending on the probability value. The experiments showed that adjusting the effect of the regularization parameter on different image regions produces better segmentation results than using a single best regularization parameter.

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Author information

Authors and Affiliations

  1. Computer Engineering Department, Computer Vision Lab., Gebze Institute of Technology, Kocaeli, Turkey

    Sema Candemir & Yusuf Sinan Akgül

Authors
  1. Sema Candemir
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  2. Yusuf Sinan Akgül
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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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© 2010 Springer-Verlag Berlin Heidelberg

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Candemir, S., Akgül, Y.S. (2010). Adaptive Regularization Parameter for Graph Cut 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_13

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  • DOI: https://doi.org/10.1007/978-3-642-13772-3_13

  • 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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