Abstract
In this paper we propose a new segment-based stereo matching algorithm using scene hierarchical structure. In particular, we highlight a previously overlooked geometric fact: the most foreground objects can be easily detected by intensity-based cost function and the farer objects can be matched using local occlusion model constructed by former recognized objects. Then the scene structure is achieved from foreground to background. Two occlusion relations are proposed to establish occlusion model and to update cost function. Image segmentation technique is adopted to increase algorithm efficiency and to decrease discontinuity of disparity map. Experiments demonstrate that the performance of our algorithm is among the state of the art stereo algorithms on various data sets.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47, 7–42 (2002)
Brown, M.Z., Hager, G.D.: Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 993–1008 (2003)
Okutomi, M., Kanade, T.: A multiple baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 353–363 (1993)
Fusiello, A., Roberto, V., Trucco, E.: Efficient stereo with multiple windowing. In: IEEE Computer Vision and Pattern Recognition, pp. 858–863 (1997)
Geiger, D., Ladendorf, B., Yuille, A.: Occlusions and binocular stereo. In: European Conference on Computer Vision, pp. 425–433 (1992)
Veksler, O.: Fast variable window for stereo correspondence using integral images. In: IEEE Computer Vision and Pattern Recognition, pp. 556–564 (2003)
Birchfield, S., Tomasi, C.: Depth discontinuities by pixel-to-pixel stereo. International Journal of Computer Vision 35, 269–299 (1999)
Bobick, A.F., Intille, S.S.: Large occlusion stereo. International Journal of Computer Vision 33, 181–200 (1999)
Gong, M., Yang, Y.H.: Fast stereo matching using reliability-based dynamic programming and consistency constraints. In: IEEE International Conference on Computer Vision, pp. 610–617 (2003)
Cormen, T.H., Leiserson, C.E., Stein, C.: Introduction to algorithms. Higher Education Press and The MIT Press, Beijing (2002)
Roy, S.: Stereo without epipolar lines: A maximum-flow formulation. International Journal of Computer Vision 34, 147–161 (1999)
Birchfield, S., Tomasi, C.: Multiway cut for stereo and motion with slanted surfaces. In: IEEE International Conference on Computer Vision, pp. 489–495 (1999)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1222–1239 (2001)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut / max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1124–1137 (2004)
Tao, H., Sawhney, H., Kumar, R.: A global matching framework for stereo computation. In: IEEE International Conference on Computer Vision, pp. 532–539 (2001)
Wei, Y., Quan, L.: Region-based progressive stereo matching. In: IEEE Computer Vision and Pattern Recognition, pp. 106–113 (2004)
Hong, L., Chen, G.: Segment-based stereo matching using graph cuts. In: IEEE Computer Vision and Pattern Recognition, pp. 74–81 (2004)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kai, Z., Yuzhou, W., Guoping, W. (2006). Hierarchical Stereo Matching: From Foreground to Background. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_58
Download citation
DOI: https://doi.org/10.1007/11864349_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44630-9
Online ISBN: 978-3-540-44632-3
eBook Packages: Computer ScienceComputer Science (R0)