Abstract
A probabilistic algorithm is presented for finding correspondences across multiple images in systems with large numbers of cameras and considerable overlap. The algorithm employs the theory of random graphs to provide an efficient probabilistic algorithm that performs Wide-baseline Stereo (WBS) comparisons on a small number of image pairs, and then propagates correspondence information among the cameras. A concrete mathematical analysis of its performance is given. The algorithm is extended to handle false-positive and false-negative failures of the WBS computations. We characterize the detectability of the existence of such failures, and propose an efficient method for this detection. Based on this, we propose a heuristic method for discarding false matches, and demonstrate its effectiveness in reducing errors.
Since in many multi-camera applications cameras are attached to processors that handle local processing and communication, it is natural to consider distributed solutions that make use of the local processors and do not use a central computer. Our algorithm is especially suited to run in a distributed setting. If the local processors are sufficiently powerful, this allows an order of magnitude increase in computational efficiency. More importantly, a distributed implementation provides strong robustness guarantees, and eliminates the existence of a single point of failure that is inherent when the application is coordinated by a central computer. We show how to efficiently overcome processor crashes and communication failures with a minimal reduction in the quality of the algorithm’s results.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Avidan, S., Moses, Y., and Moses, Y. 2004. Probabilistic multi-view correspondence in a distributed setting with no central server. In Proc. of European Conference of Computer Vision, pp. 428–441.
Baumberg, A. 2000. Reliable feature matching across widely separated views. In Proc. of IEEE Computer Vision and Pattern Recognition, pp. I:774–781.
Blum, A., Bansal, N., and Chawla, S. 2004. Correlation clustering. In Machine Learning 56(1-3):89–113.
Bollobás, B. 2001. Random Graphs, 2nd edition. Cambridge University Press.
Bollobás, B. and Thomason, A.G. 1985. Random graphs of small order. In Random Graphs, Annals of Discrete Mathematics.
Brumitt, B., Krumm, J., Meyers, B., and Shafer, S. 2000. Ubiquitous computing and the role of geometry. IEEE Wireless Communications 7(5):41–43.
Cai, Q. and Aggarwal, J. 1998. Automatic tracking of human motion in indoor scenes across multiple synchronized video streams. In International Conference on Computer Vision, pp. 356–362.
Chetverikov, D. and Matas, J. 2002. Periodic textures as distinguished regions for wide-baseline stereo correspondence. In Texture, pp. 25–30.
Collins, R., Lipton, A., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., and Hasegawa, O. 2000. A system for video surveillance and monitoring. CMU-RI-TR-00-12.
Collins, R. and Tsin, Y. 1999. Calibration of an outdoor active camera system. In Proc. of IEEE Computer Vision and Pattern Recognition, pp. 528–534.
Erdös, P. and Rényi, A. 1959. On random graphs Publ. Math. Debrecen, 6:290–297.
Ferrari, V., Tuytelaars, T., and Gool, L.V. 2003. Wide-baseline multiple-view correspondences. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 718– 725.
Kang, J., Cohen, I., and Medioni, G. 2002. Continuous multi-views tracking using tensor voting. In Motion 02, pp. 181–186.
Karuppiah, D., Zhu, Z. Shenoy, P., and Riseman, E. 2001. A fault-tolerant distributed vision system architecture for object tracking in a smart room. In International Workshop on Computer Vision Systems, pp. 201–219.
Levi, N. and Werman, M. 2003. The viewing graph. In Proc. of IEEE Computer Vision and Pattern Recognition, pp. 518–524.
Lopez de Ipina, D., Mendonca, P.R.S., and Hopper, A. 2002. Trip: A low-cost vision-based location system for ubiquitous computing. In Personal and Ubiquitous Computing Journal, 6(3):206–219.
Lynch, N.A. 1996. Distributed Algorithms. MIT Press.
M.A. Fischler, R. C.B. 1981. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. In Communication of the ACM, vol. 24, pp. 381– 395.
Matas, J., Chum, O., Urban, M., and Pajdla, T. 2002. Robust wide baseline stereo from maximally stable extremal regions. In The British Machine Vision Conference.
Mikolajczyk, K. and Schmid, C. 2004. Scale & affine invariant interest point detectors. Int. J. Comput. Vision 60, 1: 63–86.
Mittal, A. and Davis, L. 2003. M2tracker: A multi-view approach to segmenting and tracking people in a cluttered scene. IJCV 51, 3: 189–203.
Mullender, S. 1993. Distributed Systems. Addison Wesley.
Narayanan, P., Kanade, T., and Rander, P. 1995. Virtualized reality: Concepts and early results. In Proc. IEEE Workshop on Representation of Visual Scenes, pp. 69–76.
Pritchett, P. and Zisserman, A. 1998. Wide baseline stereo matching. In Proc. International Conference on Computer Vision, pp. 754–760.
Rahimi, M., Estrin, D., Baer, R., Uyeno, H., and Warrior, J. 2004. Cyclops, image sensing and interpretation in wireless networks. In Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, pp. 311–311.
Saito, H., Baba, S., Kimura, M., Vedula, S., and Kanade, T. 1999. Appearance-based virtual view generation of temporally-varying events from multi-camera images in the 3d room. In Proc. of Second International Conference on 3-D Digital Imaging and Modeling, pp. 516–525.
Schaffalitzky, F. and Zisserman, A. 2001. Viewpoint invariant texture matching and wide baseline stereo. In Proc. International Conference on Computer Vision, pp. II:636–643.
Schaffalitzky, F. and Zisserman, A. 2002. Multi-view matching for unordered image sets, or how do I organize my holiday snaps? In Proc. of European Conference of Computer Vision, pp. 414–431.
Tanenbaum, A.S. and van Steen, M. 2001. Distributed Systems Principles and Paradigms. Pearson Education Publisher.
Triggs, W., McLauchlan, P., Hartley, R., and Fitzgibbon, A. 1999. Bundle adjustment—a modern synthesis. In Vision Algorithms: Theory and Practice, pp. 298–372.
Tuytelaars, T. and Van Gool, L. 2000. Wide baseline stereo matching based on local, affinely invariant regions. In The British Machine Vision Conference, pp. 412–425.
Wren, C. and Rao, S. 2003. Self-configuring, lightweight sensor networks for ubiquitous computing. In Proc. of International Conference on Ubiquitous Computing (UbiComp.), pp. 205–206.
Author information
Authors and Affiliations
Corresponding author
Additional information
This paper is an extension of Avidan et al. (2004), which appeared in ECCV-04.
Rights and permissions
About this article
Cite this article
Avidan, S., Moses, Y. & Moses, Y. Centralized and Distributed Multi-view Correspondence. Int J Comput Vision 71, 49–69 (2007). https://doi.org/10.1007/s11263-005-4888-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11263-005-4888-y