A Robust Stereo Matching Algorithm Based on Disparity Growing of Non-Horizontal Edge Features

In this paper, a robust stereo matching technique using growing component seeds has been proposed in order to find a semi-dense disparity map. In the proposed algorithm only a fraction of the disparity space is checked to find a semi-dense disparity map. The method is a combination of both the feature based and the area based matching algorithms. The features are the non-horizontal edges extracted by the proposed feature detection algorithm. In order to compute the disparity values in non-feature points, a feature growing algorithm has been applied. The quality of correspondence seeds influences the computing time and the quality of the final disparity map. We show that the proposed algorithm achieves similar results as the ground truth disparity map. The proposed algorithm has been further compared with box filtering, belief propagation, random Growing-Component-Seeds and Canny-Growing-Component-Seeds. According to the obtained results, the proposed method increases the speed of computation ratio of the random GCS algorithm by 31% and the ratio of canny GCS algorithm by 13%. The experimental results show the proposed method achieves higher speed, more accurate disparity map, and the lowest RMS errors.