A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation
SMBV '01 Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV'01)
Obstacle Detection with Stereo Vision for Off-Road Vehicle Navigation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Local Stereo Matching with Segmentation-based Outlier Rejection
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Localization of ahead vehicles with on-board stereo cameras
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Recent progress in road and lane detection: a survey
Machine Vision and Applications
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The estimation of road profiles from low texture stereo images is a problematic task because the disparity images computed from such class of images have a large number of noisy disparities. This paper presents a new method that is based on edge maps to guide the cost aggregation process in the stereo matching problem. Using the proposed aggregation method, the disparity images are smooth at low texture regions, but the boundaries of on-road objects are still preserved. The V-disparity images computed from such reliable disparity images can clearly show the road profiles. Thereby, the road profiles can be straightforwardly extracted by the dynamic programming technique. Experiments on a long and real stereo image sequence demonstrate that the proposed method can robustly estimate the road profiles. Furthermore, on-road objects can be detected by combining v- and u-disparity images as well because their boundaries are preserved in the disparity images.