Abstract
We present a new bio-inspired approach applied to a problem of stereo images matching. This approach is based on an artifical epidemic process, that we call “the infection algorithm.” The problem at hand is a basic one in computer vision for 3D scene reconstruction. It has many complex aspects and is known as an extremely difficult one. The aim is to match the contents of two images in order to obtain 3D informations which allow the generation of simulated projections from a viewpoint that is different from the ones of the initial photographs. This process is known as view synthesis. The algorithm we propose exploits the image contents in order to only produce the necessary 3D depth information, while saving computational time. It is based on a set of distributed rules, that propagate like an artificial epidemy over the images. Experiments on a pair of real images are presented, and realistic reprojected images have been generated.
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
Abbey, H.: An Examination of the Reed Frost Theory of Epidemics. Human Biology 24, 201–233 (1952)
Brown, M.Z., Burschka, D., Hager, G.D.: Advances in Computational Stereo. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(8), 993–1008 (2003)
Fielding, G., Kam, M.: Weighted Matchings for Dense Stereo Correspondence. Pattern Recognition 33, 1511–1524 (2000)
Ganesh, A.J., Kermarrec, A.-M., Massoulié, L.: Scamp: Peer-topeer lightweight membership service for large-scale group communication. In: Crowcroft, J., Hofmann, M. (eds.) NGC 2001. LNCS, vol. 2233, pp. 44–55. Springer, Heidelberg (2001)
Kermark, W.O., McKendrick, A.G.: A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A 115(772), 700–721 (1927)
Louchet, J.: Using an Individual Evolution Strategy for Stereovision. Genetic Programming and Evolvable Machines Journal 2(2), 101–109 (2001)
Luo, Q., Zhou, J., Yu, S., Xiao, D.: Stereo Matching and Occlusion Detection with Integrity and Illusion Sensitivity. Pattern Recognition Letters 24, 1143–1149 (2003)
Maniatty, W., Szymanski, B., Caraco, T.: Parallel Computing with Generalized Cellular Automata. Nova Science Publishers, Inc., Bombay (2001)
Maniatty, W., Szymanski, B.K., Caraco, T.: Epidemics Modeling and Simulation on a Parallel Machine. In: IASTED, editor Proceedings of the International Conference on Applied Modeling and Simulation, Vancouver, Canada, pp. 69–70 (1993)
Moore, C., Newman, M.E.J.: Epidemics and Percolation in Small-World Networks. Phys. Rev. E 61, 5678–5682 (2000)
Olague, G.: Automated Photogrammetric Network Design using Genetic Algorithms. Photogrammetric Engineering & Remote Sensing 68(5), 423–431 (2002)
Olague, G., Hernández, B., Dunn, E.: Accurate L-Corner Measurement using USEF Functions and Evolutionary Algorithms. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 410–421. Springer, Heidelberg (2003)
Olague, G., Hernández, B.: A New Accurate and Flexible Model Based Multi-corner Detector for Measurement and Recognition. Pattern Recognition Letters (to appear)
Keysers, D., Unger, W.: Elastic Image Matching is NP-complete. Pattern Recognition Letters 24(1-3), 445–453 (2003)
Sipper, M.: Evolution of Parallel Cellular Machines. Springer, Heidelberg (1997)
Sun, J., Zheng, N.-N., Shum, H.-Y.: Stereo Matching using Belif Propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(7), 787–800 (2003)
Watts, D.J.: Small Worlds. Princeton University Press, Princeton (1999)
Lawrence Zitnick, C., Kanade, T.: A Cooperative Algorithm for Stereo Matching and Occlusion Detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(7), 675–684 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Olague, G., de Vega, F.F., Pérez, C.B., Lutton, E. (2004). The Infection Algorithm: An Artificial Epidemic Approach for Dense Stereo Matching. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_63
Download citation
DOI: https://doi.org/10.1007/978-3-540-30217-9_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23092-2
Online ISBN: 978-3-540-30217-9
eBook Packages: Springer Book Archive