BMVC 2004, Kingston, 7th-9th Sept, 2004
A method for learning matching errors for stereo computation
D. Kong and H.Tao (University of California, Santa Cruz)
This paper describes a novel learning-based approach for improving the performance of stereo
computation. It is based on the observation that whether the image matching scores lead to true or
erroneous depth values is dependent on the original stereo images and the underlying scene structure.
This function is learned from training data and is integrated into a depth estimation algorithm using the
MAP-MRF framework. Because the resultant likelihood function is dependent on the states of a large
neighboring region around each pixel, we propose to solve the high-order MRF inference problem using
the simulated annealing algorithm combined with a Metropolis-Hastings sampler. A segmentation-based
approach is proposed to accelerate the computational speed and improve the performance. Preliminary
experimental results show that the learning process captures common errors in SSD matching including
the fattening effect, the aperture effect, and mismatches in occluded or low texture regions. It is also
demonstrated that the proposed approach significantly improves the accuracy of the depth computation.