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A fast rich information-based stereo matching framework

  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the recent development on image affine region descriptors, we can extract more salient and useful local information from images. That information can be used to help us to better solve a fundamental problem in computer vision - stereo vision. In this paper we propose a framework for stereo matching problems in order to give a rich-information based, high-precision and fast solution. Affine regions based SIFT are chosen as features rather than point features to extract more information. In the matching period, a search algorithm with incremental dissimilarity approximations is used for efficient computing. For correctness, MLESAC(Maximum Likelihood Estimation Sample Consensus) method is used to eliminate outliers. In the experiment part, we evaluate different combinations on the performance of speed, correctness and transformations.

Original languageEnglish
Title of host publicationProceedings of the 2009 Chinese Conference on Pattern Recognition, CCPR 2009, and the 1st CJK Joint Workshop on Pattern Recognition, CJKPR
Pages237-241
Number of pages5
DOIs
StatePublished - 2009
Event2009 Chinese Conference on Pattern Recognition, CCPR 2009 and the 1st CJK Joint Workshop on Pattern Recognition, CJKPR - Nanjing, China
Duration: 4 Nov 20096 Nov 2009

Publication series

NameProceedings of the 2009 Chinese Conference on Pattern Recognition, CCPR 2009, and the 1st CJK Joint Workshop on Pattern Recognition, CJKPR

Conference

Conference2009 Chinese Conference on Pattern Recognition, CCPR 2009 and the 1st CJK Joint Workshop on Pattern Recognition, CJKPR
Country/TerritoryChina
CityNanjing
Period4/11/096/11/09

Keywords

  • Affine region
  • IDA(Incremental Dissimilarity Approximations)
  • MLESAC
  • SIFT
  • Stereo matching

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