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An Energy Minimisation Approach to Attributed Graph Regularisation

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4679))

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Abstract

In this paper, we propose a novel approach to graph regularisation based on energy minimisation. Our method hinges in the use of a Ginzburg-Landau functional whose extremum is achieved efficiently by a gradient descend optimisation process. As a result of the treatment given in this paper to the regularisation problem, constraints can be enforced in a straightforward manner. This provides a means to solve a number of problems in computer vision and pattern recognition. To illustrate the general nature of our graph regularisation algorithm, we show results on two application vehicles, photometric stereo and image segmentation. Our experimental results demonstrate the efficacy of our method for both applications under study.

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Author information

Authors and Affiliations

  1. Department of Information Engineering, ANU, Canberra, Australia

    Zhouyu Fu & Antonio Robles-Kelly

  2. NICTA RSISE Bldg. 115, Australian National University, ACT 0200, Australia

    Antonio Robles-Kelly

Authors
  1. Zhouyu Fu
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  2. Antonio Robles-Kelly
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Editor information

Alan L. Yuille Song-Chun Zhu Daniel Cremers Yongtian Wang

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© 2007 Springer-Verlag Berlin Heidelberg

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Fu, Z., Robles-Kelly, A. (2007). An Energy Minimisation Approach to Attributed Graph Regularisation. In: Yuille, A.L., Zhu, SC., Cremers, D., Wang, Y. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2007. Lecture Notes in Computer Science, vol 4679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74198-5_6

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  • DOI: https://doi.org/10.1007/978-3-540-74198-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74195-4

  • Online ISBN: 978-3-540-74198-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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