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Multiclass Segmentation by Iterated ROF Thresholding

  • Conference paper
Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8081))

Abstract

Variational models as the Mumford-Shah model and the active contour model have many applications in image segmentation. In this paper, we propose a new multiclass segmentation model by combining the Rudin-Osher-Fatemi model with an iterative thresholding procedure. We show that our new model for two classes is indeed equivalent to the Chan-Vese model but with an adapted regularization parameter which allows to segment classes with similar gray values. We propose an efficient algorithm and discuss its convergence under certain conditions. Experiments on cartoon, texture and medical images demonstrate that our algorithm is not only fast but provides very good segmentation results in comparison with other state-of-the-art segmentation models in particular for images containing classes of similar gray values.

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Authors and Affiliations

  1. Department of Mathematics, University of Kaiserslautern, Kaiserslautern, Germany

    Xiaohao Cai & Gabriele Steidl

Authors
  1. Xiaohao Cai
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  2. Gabriele Steidl
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Editor information

Editors and Affiliations

  1. Centre for Mathematical Sciences, Lund University, Sweden

    Anders Heyden , Fredrik Kahl , Carl Olsson  & Magnus Oskarsson , ,  & 

  2. Dept. of Mathematics, University of Bergen, Johaness Brunsgate 12, 5007, Bergen, Norway

    Xue-Cheng Tai

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Cai, X., Steidl, G. (2013). Multiclass Segmentation by Iterated ROF Thresholding. In: Heyden, A., Kahl, F., Olsson, C., Oskarsson, M., Tai, XC. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2013. Lecture Notes in Computer Science, vol 8081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40395-8_18

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  • DOI: https://doi.org/10.1007/978-3-642-40395-8_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40394-1

  • Online ISBN: 978-3-642-40395-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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