A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Dense Structure-from-Motion: An Approach Based on Segment Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Occlusion filling in stereo: Theory and experiments
Computer Vision and Image Understanding
Hi-index | 0.00 |
In this paper, we propose a new stereo matching algorithm using an iterated graph cuts and mean shift filtering technique. Our algorithm consists of following two steps. In the first step, given an estimated sparse RDM (Reliable Disparity Map), we obtain an updated dense disparity map through a new constrained energy minimization framework that can cope with occlusion. The graph cuts technique is employed for the solution of the proposed stereo model. In the second step, we re-estimate the RDM from the disparity map obtained in the first step. In order to obtain accurate reliable disparities, the crosschecking technique followed by the mean shift filtering in the color-disparity space is introduced. The proposed algorithm expands the RDM repeatedly through the above two steps until it converges. Experimental results on the standard data set demonstrate that the proposed algorithm achieves comparable performance to the state-of-the-arts, and gives good results especially in the areas such as the disparity discontinuous boundaries and occluded regions, where the conventional methods usually suffer.