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Robust real-time pattern matching using bayesian sequential hypothesis testing.

Ofir Pele1, Michael Werman

  • 1School of Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel. ofirpele@cs.huji.ac.il

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 21, 2008
PubMed
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This study introduces a robust real-time pattern matching method using a novel Image Hamming Distance Family and Bayesian framework. The developed algorithm achieves excellent performance, even with occluded and transformed images, enabling rapid pattern detection.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Real-time pattern matching is crucial for various applications.
  • Existing methods struggle with image variations like occlusion and deformation.
  • Robustness to image transformations remains a challenge.

Purpose of the Study:

  • To develop a robust real-time pattern matching method.
  • To introduce a novel family of image distance measures.
  • To present a Bayesian framework for efficient hypothesis testing.

Main Methods:

  • Introduction of the "Image Hamming Distance Family" for image comparison.
  • Development of a Bayesian framework for sequential hypothesis testing.
  • Design of optimal and near-optimal rejection/acceptance sampling algorithms.

Related Experiment Videos

Main Results:

  • The Image Hamming Distance Family demonstrates robustness to occlusion, geometric transforms, light changes, and non-rigid deformations.
  • The sequential sampling algorithm achieves excellent performance in pattern detection.
  • Real-time detection of a 2197-pixel pattern in 640x480 frames, with rotation and occlusion, achieved 0.022 seconds per frame.

Conclusions:

  • The proposed method offers a robust and efficient solution for real-time pattern matching.
  • The Bayesian framework and sampling algorithms significantly improve detection speed and accuracy.
  • This approach is highly effective even under challenging image conditions.