Acta Optica Sinica, Volume. 39, Issue 3, 0315001(2019)
Dense Stereo Matching Algorithm Based on Image Segmentation
A dense stereo matching algorithm is proposed based on image segmentation. This algorithm combines the gray-gradient algorithm and the zero-mean normalized cross-correlation (ZNCC) algorithm to generate matching cost. The SLIC (Simple Liner Iterative Cluster) algorithm is used for image segmentation. A method based disparity map and superpixels is proposed to update the matching cost. At the disparity post-processing stage, the LRC (Left Right Check), hole filling and cross adaptive window weighted median filtering methods are used to reduce the error matching rate of the disparity map. The performance evaluation experiments on four Middlebury stereo pairs demonstrate that the proposed algorithm achieves an average error matching rate of 4.99%.
Get Citation
Copy Citation Text
Ruihao Ma, Feng Zhu, Qingxiao Wu, Rongrong Lu, Jingyang Wei. Dense Stereo Matching Algorithm Based on Image Segmentation[J]. Acta Optica Sinica, 2019, 39(3): 0315001
Category: Machine Vision
Received: Sep. 13, 2018
Accepted: Oct. 21, 2018
Published Online: May. 10, 2019
The Author Email: