Cross Image Inference Scheme for Stereo Matching

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7727)


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.


Reference Image Source Image Neighboring Pixel Markov Random Field Turbo Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  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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