Stereo vision correspondence selection using mutual information and the development of a parametric framework
Fookes, Clinton B., Lamanna, Arvin, & Bennamoun, Mohammed (2001) Stereo vision correspondence selection using mutual information and the development of a parametric framework. In Novins, Kevin & McCane, Brendan (Eds.) Image and Vision Computing, 26-28 November 2001, Dunedin, New Zealand.
This is the latest version of this eprint.
This paper addresses the problem of correspondence selection in stereo vision using the mutual information (MI) measure. MI is an information theoretic topic that has recently seen a prolific expansion in the computer vision and medical imaging field. Two main issues are considered in this paper. Firstly, a new stereo matching algorithm is presented that uses a histogram-based formulation of mutual information. It also incorporates adaptive window sizes based on the amount of information contained in the candidate and template windows. Secondly, we present a discussion on some recent work on developing a parametric framework for MI stereo matching. This involves estimating parameters of the underlying probability density functions and then solving for the entropy of the estimated densities to compute the MI measure. The form used for the density functions are the generalised Laplace distribution and a Gaussian mixture model. Entropy calculations are then discussed using analytic methods and also a numerical Gauss-Hermite quadrature technique. Experimental results for the histogram-based stereo matching technique indicate that the mutual information measure shows significant promise due to its robustness in overcoming the effects of radiometric distortion. Preliminary results are also shown for the parametric method.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Stereo Vision, Mutual Information, Parametric|
|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:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2001 [please consult the author]|
|Deposited On:||17 Feb 2009 23:16|
|Last Modified:||13 Jul 2016 21:45|
Available Versions of this Item
Stereo Vision Correspondence Selection usign Mutual Information and the Development of a Parametric Framework. (deposited 17 Jun 2009 14:46)
- Stereo vision correspondence selection using mutual information and the development of a parametric framework. (deposited 17 Feb 2009 23:16) [Currently Displayed]
Repository Staff Only: item control page