Learning and Feature Selection in Stereo Matching

Michael S. Lew, Thomas S. Huang, Kam Wong

Research output: Contribution to journalArticlepeer-review


We present a novel stereo matching algorithm which integrates learning, feature selection, and surface reconstruction. First, a new instance based learning (IBL) algorithm is used to generate an approximation to the optimal feature set for matching. In addition, the importance of two separate kinds of knowledge, image dependent knowledge and image independent knowledge, is discussed. Second, we develop an adaptive method for refining the feature set. This adaptive method analyzes the feature error to locate areas of the image that would lead to false matches. Then these areas are used to guide the search through feature space towards maximizing the class separation distance between the correct match and the false matches. Third, we introduce a self-diagnostic method for determining when apriori knowledge is necessary for finding the correct match. If the a priori knowledge is necessary then we use a surface reconstruction model to discriminate between match possibilities. Our algorithm is comprehensively tested against fixed feature set algorithms and against a traditional pyramid algorithm. Finally, we present and discuss extensive empirical results of our algorithm based on a large set of real images.

Original languageEnglish (US)
Pages (from-to)869-881
Number of pages13
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number9
StatePublished - Sep 1994


  • Learning
  • automated terrain modeling
  • error analysis
  • matching
  • optimal feature selection
  • range finding
  • self-diagnosis and image dependent knowledge
  • stereo

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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