Efficient binocular stereo matching based on SAD and improved census transformation

Zhang, Yun, Chen, Wenxiang, Liu, Han ORCID: https://orcid.org/0000-0002-7731-8258, Liu, Jinhua and Du, Hui 2019. Efficient binocular stereo matching based on SAD and improved census transformation. Presented at: International Conference on Machine Learning and Cybernetics, Kobe, Japan, 7-10 July 2019. 2019 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 10.1109/ICMLC48188.2019.8949324

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Abstract

Binocular stereo matching aims to obtain disparities from two very close views. Existing stereo matching methods may cause false matching when there are much image noise and disparity discontinuities. This paper proposes a novel binocular stereo matching algorithm based on SAD and improved Census transformation. We first perform improved Census transformation, and then get the matching costs by combining SAD and improved Census transformation. Finally we cluster the matching costs and calculate the disparities. To generate better disparities, we further propose the improved bilateral and selective filters to enhance the accuracy of disparities. Experimental results show that our binocular stereo matching can produce more accurate and complete disparities, and works well in complex scenes with irregular shapes and more objects , thus has wide applications in stereoscopic image processing.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	IEEE
ISBN:	9781728128177
ISSN:	2160-133X
Related URLs:	Organisation
Date of First Compliant Deposit:	19 July 2019
Date of Acceptance:	21 May 2019
Last Modified:	07 Dec 2022 14:00
URI:	https://orca.cardiff.ac.uk/id/eprint/124287

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