Fast and robust stereo matching algorithms for mining automation

Banks, Jasmine, Bennamoun, Mohammed, & Corke, Peter (1997) Fast and robust stereo matching algorithms for mining automation. In Pan, Heping, Brooks, Mike, McMichael, Daniel, & Newsam, Gary (Eds.) Proceedings of the International Workshop Image Analysis and Information Fusion 1997, CSSIP - Cooperative Research Centre for Sensor Signal and Information Processing, Adelaide, SA, pp. 139-149.

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Abstract

The mining environment, being complex, irregular and time varying, presents a challenging prospect for stereo vision. The objective is to produce a stereo vision sensor suited to close-range scenes consisting primarily of rocks. This sensor should be able to produce a dense depth map within real-time constraints. Speed and robustness are of foremost importance for this investigation. A number of area based matching metrics have been implemented, including the SAD, SSD, NCC, and their zero-meaned versions. The NCC and the zero meaned SAD and SSD were found to produce the disparity maps with the highest proportion of valid matches. The plain SAD and SSD were the least computationally expensive, due to all their operations taking place in integer arithmetic, however, they were extremely sensitive to radiometric distortion. Non-parametric techniques for matching, in particular, the rank and the census transform, have also been investigated. The rank and census transforms were found to be robust with respect to radiometric distortion, as well as being able to produce disparity maps with a high proportion of valid matches. An additional advantage of both the rank and the census transform is their amenability to fast hardware implementation.

Impact and interest:

9 citations in Web of Science®
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ID Code: 55367
Item Type: Conference Paper
Refereed: Yes
Additional Information: Paper also published in Digital Signal Processing, Vol.9(3), pp. 137–148
Keywords: stereo vision, image matching, area-based matching, rank transform, census transform
ISBN: 0646330691
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 1997 The Authors
Deposited On: 11 Dec 2012 00:38
Last Modified: 12 Dec 2012 00:04

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