Stereo correspondence using efficient hierarchical belief propagation

Raj Kumar Gupta, Siu Yeung Cho

Research output: Journal PublicationArticlepeer-review

8 Citations (Scopus)

Abstract

In this paper, a new algorithm is presented to compute the disparity map from a stereo pair of images by using Belief Propagation (BP). While many algorithms have been proposed in recent years, the real-time computation of an accurate disparity map is still a challenging task. The computation time and run-time memory requirements are two very important factors for all real-time applications. The proposed algorithm divides the matching process into two steps; they are initial matching and disparity map refinement. Initial matching is performed by memory efficient hierarchical belief propagation algorithm that uses less than half memory at run-time and minimizes the energy function at much faster rate as compare to other hierarchical BP algorithms that makes it more suitable for real-time applications. Disparity map refinement uses a simple but very effective single-pass approach that improves the accuracy without affecting the computation cost. Experiments by using Middlebury dataset demonstrate that the performance of our algorithm is the best among other real-time stereo matching algorithms.

Original languageEnglish
Pages (from-to)1585-1592
Number of pages8
JournalNeural Computing and Applications
Volume21
Issue number7
DOIs
Publication statusPublished - Oct 2012
Externally publishedYes

Keywords

  • Disparity map refinement
  • Hierarchical belief propagation
  • Stereo vision

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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