Edge Detection with Embedded Confidence
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Segmentation and Matching in Stereo Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
ZNCC-based template matching using bounded partial correlation
Pattern Recognition Letters
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Active stereo vision-based mobile robot navigation for person tracking
Integrated Computer-Aided Engineering - Informatics in Control, Automation and Robotics
Autonomous robot motion in an unknown environment by local 3D elevation map construction
Integrated Computer-Aided Engineering - Informatics in Control, Automation and Robotics
A color topographic map based on the dichromatic reflectance model
Journal on Image and Video Processing - Color in Image and Video Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear disparity mapping for stereoscopic 3D
ACM SIGGRAPH 2010 papers
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Mixed color/level lines and their stereo- matching with a modified Hausdorff distance
Integrated Computer-Aided Engineering
Automatic image search based on improved feature descriptors and decision tree
Integrated Computer-Aided Engineering
Ciratefi: An RST-invariant template matching with extension to color images
Integrated Computer-Aided Engineering
3C Vision: Cues, Context and Channels
3C Vision: Cues, Context and Channels
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
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Depth perception is a crucial but greedy task for most mobile robots to navigate in their environment and avoid obstacles. Generally, it does not need to be completed at permanent full precision but can be done in a coarse-to-fine strategy. In the present paper, a novel method is proposed to enhance a very sparse disparity map provided by a block matching strategy for example. To that purpose, the region and edge maps of the initial image are successively analyzed. The segmentation is achieved on the sole chrominance information to produce stable and homogeneous regions, closely related to the real boundaries of the objects. A particular attention is given to remove errors due to occlusions, by making the depth and region maps cooperate. In a second stage, the luminance variations are analyzed to fill up the depth map. Finally, the edge map computed on the luminance component is used to alleviate problems due to under-segmentation. The experiments show that the initial disparity map is successfully improved without any minimization process. A comparison is made regarding the segmentation method, whether it performs directly in the spatial domain region-growing or it jointly uses the color and space domains mean-shift. In addition, two luminance-chrominance color spaces are studied: L^* u^* v^* and ρ Φθ.