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Multi-spectral stereo image matching using Mutual Information

Fookes, Clinton B., Maeder, Anthony J., Sridharan, Sridha, & Cook, Jamie A. (2004) Multi-spectral stereo image matching using Mutual Information. In 2nd International Symposium on 3D Data Processing, Visualization, and Transmission, September 8-10, 2004, Thessaloniki, Greece.

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Abstract

Mutual information (MI) has shown promise as an effective stereo matching measure for images affected by radiometric distortion. This is due to the robustness of MI against changes in illumination. However, MI-based approaches are particularly prone to the generation of false matches due to the small statistical power of the matching windows. Consequently, most previous MI approaches utilise large matching windows which smooth the estimated disparity field. This paper proposes extensions to MI-based stereo matching in order to increase the robustness of the algorithm. Firstly, prior probabilities are incorporated into the MI measure in order to considerably increase the statistical power of the matching windows. These prior probabilities, which are calculated from the global joint histogram between the stereo pair, are tuned to a two level hierarchical approach. A 2D match surface, in which the match score is computed for every possible combination of template and matching window, is also utilised. This enforces left-right consistency and uniqueness constraints. These additions to MI-based stereo matching significantly enhance the algorithm’s ability to detect correct matches while decreasing computation time and improving the accuracy. Results show that the MI measure does not perform quite as well for standard stereo pairs when compared to traditional areabased metrics. However, the MI approach is far superior when matching across multi-spectra stereo pairs.

Impact and interest:

14 citations in Scopus
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1 citations in Web of Science®

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ID Code: 17888
Item Type: Conference Paper
Keywords: Stereo Vision, Mutual Information
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 2004 IEEE Computer Society
Deposited On: 17 Feb 2009 11:03
Last Modified: 09 Jun 2010 23:22

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