Robust real-time pattern matching using bayesian sequential hypothesis testing

Ofir Pele*, Michael Werman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


This paper describes a method for robust real time pattern matching. We first introduce a family of image distance measures, the "Image Hamming Distance Family". Members of this family are robust to occlusion, small geometrical transforms, light changes and non-rigid deformations. We then present a novel Bayesian framework for sequential hypothesis testing on finite populations. Based on this framework, we design an optimal rejection/acceptance sampling algorithm. This algorithm quickly determines whether two images are similar with respect to a member of the Image Hamming Distance Family. We also present a fast framework that designs a near-optimal sampling algorithm. Extensive experimental results show that the sequential sampling algorithm performance is excellent. Implemented on a Pentium 4 3GHz processor, detection of a pattern with 2197 pixels, in 640x480 pixel frames, where in each frame the pattern rotated and was highly occluded, proceeds at only 0.022 seconds per frame.

Original languageAmerican English
Pages (from-to)1427-1443
Number of pages17
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number8
StatePublished - Aug 2008


  • Bayesian statistics
  • Composite hypothesis
  • Finite populations
  • Hamming distance
  • Image similarity measures
  • Image statistics
  • Pattern detection
  • Pattern matching
  • Real time
  • Sequential hypothesis testing
  • Template matching


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