Suitability of non-parametric stereo matching algorithms for mining automation
Data(s) |
01/06/1998
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Resumo |
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 assesses the suitability of a number of matching techniques for use in a stereo vision sensor for close range scenes consisting primarily of rocks. These include traditional area-based matching metrics, and non-parametric transforms, in particular, the rank and census transforms. Experimental results show that the rank and census transforms exhibit a number of clear advantages over area-based matching metrics, including their low computational complexity, and robustness to certain types of distortion. |
Formato |
application/pdf |
Identificador | |
Publicador |
Australian National University, College of Engineering and Computer Science, Department of Computer Science |
Relação |
http://eprints.qut.edu.au/55373/1/AJIIPS.pdf http://cs.anu.edu.au/ojs/index.php/ajiips/index Banks, Jasmine, Bennamoun, Mohammed, Kubik, Kurt, & Corke, Peter (1998) Suitability of non-parametric stereo matching algorithms for mining automation. Australian Journal of Intelligent Information Processing Systems, 5(2), pp. 111-119. |
Direitos |
Copyright 1998 Australian National University |
Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
Palavras-Chave | #080104 Computer Vision #080106 Image Processing #stereo vision #image matching #area-based techniques #rank transform #census transform |
Tipo |
Journal Article |