Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
A compact algorithm for rectification of stereo pairs
Machine Vision and Applications
A Complete U-V-Disparity Study for Stereovision Based 3D Driving Environment Analysis
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Disparity Map Estimation Using A Total Variation Bound
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
A block-iterative surrogate constraint splitting method for quadratic signal recovery
IEEE Transactions on Signal Processing
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Obstacle detection is an important component for many autonomous vehicle navigation systems. Several methods have been proposed using various active sensors such as radar, sonar and laser range finders. Vision based techniques have the advantage of relatively low cost and provide a large amount of information about the environment around an intelligent vehicle. This paper deals with the development of an accurate and efficient vision based obstacle detection method that relies on dense disparity estimation between a pair of stereo images. Firstly, the problem of disparity estimation is formulated as that of minimizing a quadratic objective function under various convex constraints arising from prior knowledge. Then, the resulting convex optimization problem is solved via a parallel block iterative algorithm which can be efficiently implemented on parallel computing architectures. Finally, we detect obstacles from the computed depth map by performing an object segmentation based on a surface orientation criterion.