Computational geometry: an introduction
Computational geometry: an introduction
Fully dynamic Delaunay triangulation in logarithmic expected time per operation
Computational Geometry: Theory and Applications
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Artificial Intelligence - Special volume on computer vision
Stereo Matching with Nonlinear Diffusion
International Journal of Computer Vision
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
New Measurements and Corner-Guidance for Curve Matching with Probabilistic Relaxation
International Journal of Computer Vision
Match Propogation for Image-Based Modeling and Rendering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eye Gaze Correction with Stereovision for Video-Teleconferencing
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Symmetric Sub-Pixel Stereo Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
A Quasi-Dense Approach to Surface Reconstruction from Uncalibrated Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Using Monocular Cues within the Tensor Voting Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Search Space Reduction for MRF Stereo
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Walkthrough in large environments using concatenated panoramas
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Efficient disparity estimation using region based segmentation and multistage feedback
ICCOM'06 Proceedings of the 10th WSEAS international conference on Communications
Stereo matching by interpolation
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Hi-index | 0.00 |
Brute-force dense matching is usually not satisfactory because the same search range is used for the entire image, yielding potentially many false matches. In this paper, we propose a progressive scheme for stereo matching which uses two fundamental concepts: the disparity gradient limit principle and the least commitment strategy. The first states that the disparity should vary smoothly almost everywhere, and the disparity gradient should not exceed a certain limit. The second states that we should first select only the most reliable matches and therefore postpone unreliable decisions until enough confidence is accumulated. Our technique starts with a few reliable point matches obtained automatically via feature correspondence or through user input. New matches are progressively added during an iterative matching process. At each stage, the current reliable matches constrain the search range for their neighbors according to the disparity gradient limit, thereby reducing potential matching ambiguities of those neighbors. Only unambiguous matches are selected and added to the set of reliable matches in accordance with the least commitment strategy. In addition, a correlation match measure that allows rotation of the match template is used to provide a more robust estimate. The entire process is cast within a Bayesian inference framework. Experimental results illustrate the robustness of our proposed dense stereo matching approach.