A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Illumination Normalization with Time-Dependent Intrinsic Images for Video Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering Intrinsic Images from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Tracking and Stereo Matching under Variable Illumination
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Real-time Stereo Vision FPGA Chip with Low Error Rate
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
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There are growing needs in computer vision applications for stereo matching, requiring not only accuracy and robustness but also fast processing speed. Global matching algorithms such as belief propagation(BP) shows remarkably robust results with presence of occlusions, textureless region, image noise. In this paper, a novel approach that combines merits of global matching and robust matching cost is proposed. We modeled illumination difference as biased intensity model. Applying window-based matching cost which are insensitive to intensity bias, erroneous matching results under different illumination can be prevented. Moreover, adoption of memory-efficient fast belief propagation enables high speed processing with aid of parallel computing architecture. Experimental result demonstrates that our method is robust under various illumination difference.