Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure

  • Authors:
  • Andreas Klaus;Mario Sormann;Konrad Karner

  • Affiliations:
  • VRVis Research Center, Graz, Austria;VRVis Research Center, Graz, Austria;VRVis Research Center, Graz, Austria

  • Venue:
  • ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

A novel stereo matching algorithm is proposed that utilizes color segmentation on the reference image and a selfadapting matching score that maximizes the number of reliable correspondences. The scene structure is modeled by a set of planar surface patches which are estimated using a new technique that is more robust to outliers. Instead of assigning a disparity value to each pixel, a disparity plane is assigned to each segment. The optimal disparity plane labeling is approximated by applying belief propagation. Experimental results using the Middlebury stereo test bed demonstrate the superior performance of the proposed method.