Robust Real Time Pattern Matching using Bayesian Sequential Hypothesis Testing
Speaker: Michael Werman , MIT CSAIL
Date: May 9 2007
Time: 3:00PM to 4:00PM
Location: Star Seminar Room (32-D463)
Host: C. Mario Christoudias, Gerald Dalley, MIT CSAIL
Contact: C. Mario Christoudias, Gerald Dalley, 3-4278, 3-6095, cmch@csail.mit.edu, dalleyg@mit.edu
Relevant URL: Abstract
This talk 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
occlusions, geometrical transforms, light changes and non-rigid
deformations. Our second contribution is 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 sub-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.
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