Abstract
Achieving an accurate disparity map in a reasonable processing time is a real challenge in the stereovision field. For this purpose, we propose in this paper an original approach which aims to accelerate matching time while keeping a very good matching accuracy. The proposed method allows us to shift from a dense to a sparse disparity map. Firstly, we have computed scores for all pairs of pixels using a new dissimilarity function recently developed. Then, by applying a confidence measure on each pair of pixels, we keep only couples of pixels having a high confidence measure which is computed relying on a set of new local parameters.
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
Fakhfakh, N., Khoudour, L., El-Koursi, M.: Mise en Correspondance Stéréoscopique d’Images Couleur pour la Détection d’Objets Obstruant la Voie aux Passages à Niveau. In: TELECOM 2009 & 6ème JFMMA, Agadir, Maroc, p. 206 (4 pages) (2009)
Marr, D., Poggio, T.: Cooperative Computation of Stereo Disparity. American Association for the Advancement of Science 194(4262), 283–287 (1976)
Brockers, R., Hund, M., Mertsching, B.: Stereo Vision using Cost-Relaxation with 3D Support Regions. In: ICVNZ, New Zealand (2005)
Taguchi, Y., Wilburn, B., Zitnick, C.L.: Stereo Reconstruction with Mixed Pixels using Adaptive Over-Segmentation. In: CVPR, pp. 1–8, Anchorage, Alaska (2008)
Foggia, P., Jolion, J.M., Limongiello, A., Vento, M.: Stereo Vision for Obstacle Detection: A Graph-Based Approach. In: Escolano, F., Vento, M. (eds.) GbRPR 2007. LNCS, vol. 4538, pp. 37–48. Springer, Heidelberg (2007)
Lee, C., Ho, Y.: Disparity Estimation using Belief Propagation for View Interpolation. In: ITC-CSCC, Japan, pp. 21–24 (2008)
Xiong, W.H., Chung, S., Jia, J.: Fractional Stereo Matching Using Expectation-Maximization. IEEE TPAMI 31(3), 428–443 (2008)
Yoon, K.J., Kweon, S.: Adaptative Support-Weight Approach for Correspondence Search. IEEE TPAMI 28(4) (2006)
Scharstein, D., Szeliski, R.: Middlebury stereo vision research page, http://vision.middlebury.edu/stereo/
Veksler, O.: Fast Variable Window for Stereo Correspondence using Integral Image. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, Madison, Wisconsin, vol. 1, pp. 556–561 (2003)
Sun, J., Zheng, N.-N., Shum, H.Y.: Stereo Matching using Belief Propagation. IEEE TPAMI 25(7) (2003)
Klaus, A., Sormann, M., Karner, K.: Segment-Based Stereo Matching using Belief Propagation and a Self-Adapting Dissimilarity Measure. In: ICPR, pp. 15–18 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fakhfakh, N., Khoudour, L., El-Koursi, EM., Jacot, J., Dufaux, A. (2009). A New Selective Confidence Measure–Based Approach for Stereo Matching. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04595-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-04595-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04594-3
Online ISBN: 978-3-642-04595-0
eBook Packages: Computer ScienceComputer Science (R0)