Stereo Matching Based on Visual Sensitive Information
This work addresses stereo matching for computer vision applications, but it appears incremental as it builds on existing methods with specific optimizations.
The paper tackles the problem of stereo matching by proposing a cost aggregation algorithm based on dynamic windows and left-right consistency detection, which reduces the error matching rate and improves accuracy compared to the classical census algorithm.
The area of computer vision is one of the most discussed topics amongst many scholars, and stereo matching is its most important sub fields. After the parallax map is transformed into a depth map, it can be applied to many intelligent fields. In this paper, a stereo matching algorithm based on visual sensitive information is proposed by using standard images from Middlebury dataset. Aiming at the limitation of traditional stereo matching algorithms regarding the cost window, a cost aggregation algorithm based on the dynamic window is proposed, and the disparity image is optimized by using left and right consistency detection to further reduce the error matching rate. The experimental results show that the proposed algorithm can effectively enhance the stereo matching effect of the image providing significant improvement in accuracy as compared with the classical census algorithm. The proposed model code, dataset, and experimental results are available at https://github.com/WangHewei16/Stereo-Matching.