A new stereo matching method with combination of cross-based aggregation and hierarchical belief propagation

2012 ◽  
Author(s):  
Tianyu Yang ◽  
Qing He ◽  
Ning Wei ◽  
Baopu Li ◽  
Chao Hu ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Belief Propagation ◽  
Matching Method

Stereo Matching with Semi-limited Belief Propagation

2012 ◽  
Author(s):  
Chengzhi Luo ◽  
Jianjun Lei ◽  
Guanglong Hu ◽  
Kefeng Fan ◽  
Shupo Bu
Keyword(s):  
Stereo Matching ◽  
Belief Propagation

Stereo matching using belief propagation

2003 ◽  
Vol 25 (7) ◽  
pp. 787-800 ◽  
Author(s):  
Jian Sun ◽  
Nan-Ning Zheng ◽  
Heung-Yeung Shum
Keyword(s):  
Stereo Matching ◽  
Belief Propagation

scholarly journals An Efficient Dense Stereo Matching Method for Planetary Rover

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 48551-48564 ◽  
Author(s):  
Haichao Li ◽  
Liang Chen ◽  
Feng Li
Keyword(s):  
Stereo Matching ◽  
Matching Method ◽  
Planetary Rover ◽  
Dense Stereo Matching

scholarly journals Stereo Matching Algorithm Based on 2D Delaunay Triangulation

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Xue-he Zhang ◽  
Ge Li ◽  
Chang-le Li ◽  
He Zhang ◽  
Jie Zhao ◽  
...  
Keyword(s):  
Delaunay Triangulation ◽  
Stereo Matching ◽  
Robot Vision ◽  
Depth Estimation ◽  
Planning System ◽  
Disparity Map ◽  
Matching Method ◽  
Running Speed ◽  
Edge Operator ◽  
Edge Points

To fulfill the applications on robot vision, the commonly used stereo matching method for depth estimation is supposed to be efficient in terms of running speed and disparity accuracy. Based on this requirement, Delaunay-based stereo matching method is proposed to achieve the aforementioned standards in this paper. First, a Canny edge operator is used to detect the edge points of an image as supporting points. Those points are then processed using a Delaunay triangulation algorithm to divide the whole image into a series of linked triangular facets. A proposed module composed of these facets performs a rude estimation of image disparity. According to the triangular property of shared vertices, the estimated disparity is then refined to generate the disparity map. The method is tested on Middlebury stereo pairs. The running time of the proposed method is about 1 s and the matching accuracy is 93%. Experimental results show that the proposed method improves both running speed and disparity accuracy, which forms a steady foundation and good application prospect for a robot’s path planning system with stereo camera devices.

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