Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Disparity-space images and large occlusion stereo
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Occlusions and binocular stereo
International Journal of Computer Vision
Markov random field modeling in computer vision
Markov random field modeling in computer vision
A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
Tabu Search
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Stereo with Multiple Windowing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Optimal Range Segmentation Parameters through Genetic Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model and algorithm of multi-depot container truck transportation with time windows
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
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This paper presents an optimization technique to automatically select a set of control parameters for a Markov random field. The method is based on the reactive tabu search strategy, and requires to define a suitable fitness function that measures the performance of the MRF algorithm with a given parameters set. The technique is applied to stereo matching thanks to the availability of ground truth disparity maps. Experiments with synthetic and real images illustrate the approach.