Abstract
In this paper, the Dempster-Shafer Theory for uncertainty reasoning is presented as a computation tool in designing a model to approach the correspondence problem in Computer Vision. In previous works (Silva and Simoni, 2001a; Silva and Simoni, 2001b) the proposed methodology showed its effectiveness in establishing the correspondence of a pair of images with similar brightness and contrast. In this paper, the efficiency of the uncertainty reasoning methodology is evaluated by applying the method to pairs of real world images with different brightness and contrast. Contextual and structural features of a point are treated as corresponding evidences. The Dempster’s rule of combination is used to combine the existing evidences leading to an evidential interval for each candidate point. A search process maximizes the Belief on the combined evidences. The conducted experiments showed the robustness of the approach in establishing the correspondence in situations for which there is illumination and/or focus changes from one real world image to the other.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anadan, P.A., Review of Motion and Stereopsis Research. COINS Technical Report 85-52, University of Massachusetts at Amherst, December 1985.
Barnard, S. T., Thompson, W. B., Disparity Analysis of Images, IEEE PAMI 2(4), July 1980, pp. 333–334.
Giarratano, J., Riley, G., Expert Systems: Principles and Programming. Boston, PWS Publishing Company, 1994. 644 p.
Haddawy, P. A Variable Precision Logic Inference System Employing the Dempster-Shafer Uncertainty Calculus. Urbana-Illinois, MS Thesis, UILU-ENG-86-1777, 1987.
Jones, G. A., Constraint, Optimization, and Hierarchy: Reviewing Stereoscopic Correspondence of Complex Features. Computer Vision and Image Understanding 65(1), 1997, pp. 57–78.
Kanade, T., Okutomi, M., A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment. IEEE PAMI, 16(9), September 1994, pp. 920–932.
Marr, D., Vision. Freeman. San Francisco, CA. 1982.
Saito, H., Mori, M., Application of Genetic Algorithms to Stereo Matching of Images, Pattern Recognition Letters 16, 1995, pp. 815–821.
Silva, J.D.S, Simoni, P.O., Bharadwaj, K.K., A Genetic Algorithm for the Stereo Correspondence Problem in Computer Vision. Proceedings of the IASTED International Conference on Computer Graphics and Imaging. Calgary: IASTED/ACTA Press, 2000a, v. 1, p. 20–25.
Silva, J.D.S., Simoni, P.O., Bharadwaj, K.K., A Hierarchical Approach to Multiple-Point Correspondences in Stereo Vision Using a Genetic Algorithm Search. 6th Intl. Conf. on Soft Computing, IIZUKA, Fukuoka. 2000, v. 1, p. 125–130.
Silva, J.D.S., Simoni, P.O., The Island Model Parallel GA and Uncertainty Reasoning in the Correspondence Problem. In: IJCNN, Washington, DC. Proc. of IJCNN’2001, July 2001, v. 1,p. 2247–2252.
Silva, J.D.S., Simoni, P.O., Uncertainty Reasoning in the Correspondence Problem. In: IASTED-International Conference on Visualization, Imaging, and Image Processing, 2001, Marbella. Proceedings of the IASTED VIIP’2001. Anaheim, CA: ACTA Press, September 2001, v. 1, p. 647–652.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
da Silva, J.D.S., Simoni, P.O. (2002). The Correspondence Problem under an Uncertainty Reasoning Approach. In: Bittencourt, G., Ramalho, G.L. (eds) Advances in Artificial Intelligence. SBIA 2002. Lecture Notes in Computer Science(), vol 2507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36127-8_34
Download citation
DOI: https://doi.org/10.1007/3-540-36127-8_34
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00124-9
Online ISBN: 978-3-540-36127-5
eBook Packages: Springer Book Archive