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Variational Model for Image Segmentation

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Advances in Visual Computing (ISVC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8034))

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Abstract

Image segmentation is a fundamental problem in the field of image processing and computer vision with numerous applications. In the recent years, mathematical models based on partial differential equations and variational methods have led to superior results in computer vision. In this paper, we present a variational model which uses total variation as a regularization for image segmentation, and develop a primal-dual hybrid gradient algorithm to our model. We discuss images in the cases of Gaussian, Poisson and multiplicative speckle noise, the performance of our model is illustrated by experimental results on synthetic and real data. The proposed model can also preserve small anatomical structures better than Alex Sawatzky’s region-based segmentation model with less CPU times.

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Author information

Authors and Affiliations

  1. Center of Mathematical Scineces, Zhejiang University, Hangzhou, China

    Qiong Lou & Jialin Peng

  2. Department of Mathematics, Zhejiang University, Hangzhou, China

    Fa Wu & Dexing Kong

  3. The School of Computer Science and Technology, Huaqiao University, Xiamen, China

    Jialin Peng

Authors
  1. Qiong Lou
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  2. Jialin Peng
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  3. Fa Wu
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  4. Dexing Kong
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Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Arizona State University, Tempe, AZ, USA

    Baoxin Li

  6. Mitsubishi Electric Research Laboratories, Cambridge, MA, USA

    Fatih Porikli

  7. University of California, Riverside, CA, USA

    Victor Zordan

  8. AT&T Research Labs, Florham Park, NJ, USA

    James Klosowski

  9. ZIRST, Saint-Ismier Cedex, France

    Sabine Coquillart

  10. Qualcomm Research, San Diego, CA, USA

    Xun Luo

  11. Oxford e-Research Centre, University of Oxford,, Oxford, UK

    Min Chen

  12. IBM, Hawthorne, NY, USA

    David Gotz

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© 2013 Springer-Verlag Berlin Heidelberg

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Cite this paper

Lou, Q., Peng, J., Wu, F., Kong, D. (2013). Variational Model for Image Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41939-3_64

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  • DOI: https://doi.org/10.1007/978-3-642-41939-3_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41938-6

  • Online ISBN: 978-3-642-41939-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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