Stereo vision for planetary rovers: stochastic modeling to near real-time implementation
International Journal of Computer Vision
A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
A Bayesian approach to binocular stereopsis
International Journal of Computer Vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
An Experimental Comparison of Stereo Algorithms
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Object-based estimation of dense motion fields
IEEE Transactions on Image Processing
Data compression for light-field rendering
IEEE Transactions on Circuits and Systems for Video Technology
Multiview video sequence analysis, compression, and virtual viewpoint synthesis
IEEE Transactions on Circuits and Systems for Video Technology
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Correspondence analysis is required for many applications such as multimedia communication and 3-D telepresence. Current intensity-based approaches model intensity differences of corresponding points in the left- and right-eye images with Gaussian distribution. In this contribution, the statistical characteristics of intensity differences of corresponding points were studied using natural stereo images. The examination reveals that a Laplacian distribution outperforms a Gaussian distribution. Based on this result, a new approach for correspondence analysis is proposed, which exploits Laplacian distribution to model intensity differences of corresponding points. To measure the performance of different approaches, a measure related to the peak signal-to-noise ratio (PSNR) of disparity-compensated prediction over the matching ratio was introduced. The experimental results show that the proposed correspondence algorithm has a better performance than other existing approaches. It also shows that the PSNR of disparity-compensated prediction decreases as the matching ratio goes up.