@inproceedings{11733e406c5c4954801233032cf6f67f,
title = "Improved Stereo Matching Algorithm Based on Sparse Window",
abstract = "This study presents a sparse window-based stereo-matching algorithm that enhances the accuracy and efficiency of the semi-global matching algorithm. Unlike traditional methods, this algorithm processes pixel areas based on their texture features, resulting in more efficient encoding. The proposed approach systematically samples pixels within the original encoding window to reduce the number of pixels involved in the process. Additionally, using the FAST feature detection method distinguishes texture areas and applies different encoding processes for each area to obtain the feature encoding of the center pixels. Experimental results show that compared with traditional semi-global stereo matching algorithms, our proposed sparse window-based algorithm improves processing speed by 0.06 seconds and reduces average error by 10.92%.",
keywords = "census transform, matching costs, semi-global matching, stereo vision, systematic sampling",
author = "Qi Tang and Yuanping Xu and Jiliu Zhou and Chao Kong and Jin Jin and Zhijie Xu and Chaolong Zhang and Yajing Shi",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 28th International Conference on Automation and Computing, ICAC 2023 ; Conference date: 30-08-2023 Through 01-09-2023",
year = "2023",
doi = "10.1109/ICAC57885.2023.10275311",
language = "English",
series = "ICAC 2023 - 28th International Conference on Automation and Computing",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICAC 2023 - 28th International Conference on Automation and Computing",
}