Cross Image Inference Scheme for Stereo Matching

  • Xiao Tan
  • Changming Sun
  • Xavier Sirault
  • Robert Furbank
  • Tuan D. Pham
Conference paper

DOI: 10.1007/978-3-642-37447-0_17

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7727)
Cite this paper as:
Tan X., Sun C., Sirault X., Furbank R., Pham T.D. (2013) Cross Image Inference Scheme for Stereo Matching. In: Lee K.M., Matsushita Y., Rehg J.M., Hu Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7727. Springer, Berlin, Heidelberg

Abstract

In this paper, we propose a new interconnected Markov Random Field (MRF) or iMRF model for the stereo matching problem. Comparing with the standard MRF, our model takes into account the consistency between the label of a pixel in one image and the labels of its possible matching points in the other image. Inspired by the turbo decoding scheme, we formulate this consistency by a cross image reference term which is iteratively updated in our matching framework. The proposed iMRF model represents the matching problem better than the standard MRF and gives better results even without using any other information from segmentation prior or occlusion detection. We incorporate segmentation information and the coarse-to-fine scheme into our model to further improve the matching performance.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiao Tan
    • 1
    • 2
  • Changming Sun
    • 2
  • Xavier Sirault
    • 3
  • Robert Furbank
    • 3
  • Tuan D. Pham
    • 4
  1. 1.SEIT of UNSW CanberraCanberraAustralia
  2. 2.CSIRO Mathematics, Informatics and StatisticsNorth RydeAustralia
  3. 3.CSIRO Plant IndustryCanberraAustralia
  4. 4.Aizu Research Cluster for Medical Engineering and InformaticsThe University of AizuFukushimaJapan

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