Stochastic Analysis of Stereo Quantization Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
A Bayesian approach to binocular stereopsis
International Journal of Computer Vision
Bayesian Modeling of Uncertainty in Low-Level Vision
Bayesian Modeling of Uncertainty in Low-Level Vision
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Depth Estimation: A Confidence Interval Approach
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Markov Chains and Stochastic Stability
Markov Chains and Stochastic Stability
| Hi-index | 0.00 |
We propose Markov chain Monte Carlo sampling methods to address uncertainty estimation in disparity computation. We consider this problem at a postprocessing stage, i.e. once the disparity map has been computed, and suppose that the only information available is the stereoscopic pair. The method, which consists of sampling from the posterior distribution given the stereoscopic pair, allows the prediction of large errors which occur with low probability, and accounts for spatial correlations. The model we use is oriented towards an application to mid-resolution stereo systems, but we give insights on how it can be extended. Moreover, we propose a new sampling algorithm relying on Markov chain theory and the use of importance sampling to speed up the computation. The efficiency of the algorithm is demonstrated, and we illustrate our method with the computation of confidence intervals and probability maps of large errors, which may be applied to optimize a trajectory in a three dimensional environment.