Fast and robust stereo matching algorithms for mining automation

Banks, Jasmine, Bennamoun, Mohammed, Corke, Peter, & Kubik, Kurt (1998) Fast and robust stereo matching algorithms for mining automation. In Howarth, D., Gurgenci, H., Rowlands, J., Hatherly, P., Firth, B., & Meyer, T. (Eds.) 1998 Australian Mining Technology Conference, The Cooperative Research Centre for Mining Technology and Equipment (CMTE), Fremantle, WA, pp. 356-369.

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Abstract

The mining environment, being complex, irregular, and time-varying, presents a challenging prospect for stereo vision. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This paper evaluates a number of matching techniques for possible use in a stereo vision sensor for mining automation applications. Area-based techniques have been investigated because they have the potential to yield dense maps, are amenable to fast hardware implementation, and are suited to textured scenes. In addition, two nonparametric transforms, namely, rank and census, have been investigated. Matching algorithms using these transforms were found to have a number of clear advantages, including reliability in the presence of radiometric distortion, low computational complexity, and amenability to hardware implementation.

Impact and interest:

9 citations in Web of Science®
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ID Code: 55364
Item Type: Conference Paper
Refereed: Yes
Additional Information: Paper was published in Digital Signal Processing, Vol. 9(30), pp. 137–148
Keywords: stereo vision, image matching, area-based matching
ISBN: 1876315121
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 1999 Elsevier
Deposited On: 11 Dec 2012 00:32
Last Modified: 12 Dec 2012 00:06

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