Stereo Matching Based on Visual Sensitive Information

TL;DR

This paper introduces a stereo matching algorithm based on visual sensitive information that improves accuracy over classical methods by using dynamic window cost aggregation and consistency detection, validated on the Middlebury dataset.

Contribution

The paper proposes a novel stereo matching algorithm utilizing visual sensitive information with dynamic window cost aggregation and left-right consistency, enhancing accuracy.

Findings

Significant improvement in matching accuracy over classical census algorithm.

Effective reduction of error matching rate through consistency detection.

Validated results on Middlebury dataset demonstrate the method's effectiveness.

Abstract

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.