Improved Stereo Matching Algorithm Based on Sparse Window

Qi Tang, Yuanping Xu, Jiliu Zhou, Chao Kong, Jin Jin, Zhijie Xu, Chaolong Zhang, Yajing Shi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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%.

Original languageEnglish
Title of host publication2023 28th International Conference on Automation and Computing
Subtitle of host publicationICAC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350335859
ISBN (Print)9798350335866
Publication statusPublished - 16 Oct 2023
Event28th International Conference on Automation and Computing: Digitalisation for Smart Manufacturing and Systems - Aston University, Birmingham, United Kingdom
Duration: 30 Aug 20231 Sep 2023
Conference number: 28


Conference28th International Conference on Automation and Computing
Abbreviated titleICAC 2023
Country/TerritoryUnited Kingdom
Internet address

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