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COMVIS: A Communication Framework for Computer Vision

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Abstract

We describe a general approach to integrate the information produced by different visual modules with the goal of generating a quantitative 3D reconstruction of the observed scene and to estimate the reconstruction errors.

The integration is achieved in two steps. Firstly, several different visual modules analyze the scene in terms of a common data representation: planar patches are used by different visual modules to communicate and represent the 3D structure of the scene. We show how it is possible to use this simple data structure to share and integrate information from different visual modalities, and how it can support the necessities of the great majority of different visual modules known in literature. Secondly, we devise a communication scheme able to merge and improve the description of the scene in terms of planar patches. The applications of state-of-the-art algorithms allows to fuse information affected by an unknown grade of correlation and still guarantee conservative error estimates.

Tests on real and synthetic scene show that our system produces a consistent and marked improvement over the results of single visual modules, with error reduction up to a factor of ten and with typical reduction of a factor 2–4.

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References

  • Aloimonos, J. and Shulman, D. 1989. Integration of Visual ModulesAn Extension of the Marr Paradigm. Academic Press: London.

    Google Scholar 

  • Calibrated Imaging Laboratory home page. http://www.cs.cmu.edu/∼cil/cil-ster.html

  • Cozzi, A., Crespi, B., Valentinotti, F., and Wörgötter, F. 1997. Performance of pahse-based algorithms for disparity estimation. Machine Vision abd Applications, 9(5-6):334-340.

    Google Scholar 

  • Eckhorn, R., Bauer, R., Jordan, W., Kruse, B.M.W., Munk, M., and Reitböck, H. 1988. Coherent oscillations: A mechanism of feature linking in the visual cortex? Biological Cybernetics, 60:121-130.

    PubMed  Google Scholar 

  • Faugeras, O. 1993. Three-Dimensional Computer Vision. MIT Press: Cambridge.

    Google Scholar 

  • Fleet, D., Jepson, A., and Jenkin, M. 1991. Phase-based disparity measurement. Computer Vision, Graphic and Image Processing, 53(2):198-210.

    Google Scholar 

  • Fua, P., Leclerc, Y., and Loung 1999. Characterizing the performance of multiple-image point-correspondence algorithms using self-consistency. In Proceedings of: The Vision Algorithms: Theory and Practice Workshop (ICCV99), Corfu, Greece.

    Google Scholar 

  • Fua, P. and Leclerc, Y.G. 1995. Object-centered surface reconstruction: Combining multi-image stereo and shading. IJCV, 16:35-56.

    Google Scholar 

  • Gamble, E. and Poggio, T. 1987. Visual integration and detection of discontinuities: The key role of intensity edges. A.I. Memo 970, MIT, Artificial Intelligent Laboratory.

  • Geman, S. and Geman, D. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Intelligence, 6(6):721-741.

    Google Scholar 

  • Haralick, R.M. 1996. Propagating covariance in computer vision. In Workshop on Performance Characteristics of Vision Algorithms, H.I. Christensen, W. Förstner, and C.B. Madsen (Eds.), Cambridge, UK, pp. 1-12.http://www.vision.auc.dk/∼hic/performance-ws.html

  • Irani, M. and Anandan, P. 1996. Parallax geometry of pairs of points for 3d scene analysis. In Proc. 4th European Conf. on Computer Vision, Cambridge, Cambridge, UK, Vol. 1, pp. 17-30.

    Google Scholar 

  • Kanatani, K. 1990. Group-Theoretical Methods in Image Understanding, volume 20 of Springer Series in information Sciences. Springer-Verlag: Berlin, Heidelberg.

    Google Scholar 

  • Lucas, B. and Kanade, T. 1984. Optical navigation by the method of differences. In DARPA84, pp. 272-281.

  • Maybeck, P.S. 1979. Stochastic Models, Estimation, and Control, vol. 1 of Mathematics in Science and Engineering. Academic Press: New York.

    Google Scholar 

  • Opara, R. and Wörgötter, F. 1998. A fast and robust cluster update algorithm for image segmentation in spin-lattice models without annealing-visual latencies revisited. Neural Computation, 10:1547-1566.

    Google Scholar 

  • Pankanti, S. and Jain, A.K. 1995. A uniform bayesian framework for integration. Technical Report, Michigan State University.

  • Poggio, T.A., Gamble, E., and Little. J.J. 1988. Parallel integration of vision modules. Science, 242:436-439.

    Google Scholar 

  • Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. 1992. Numerical Recipes in C. Cambridge University Press, second edn.

  • Singer, W. and Gray, C.M. 1995. Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neurosci., 18:555-586.

    Google Scholar 

  • Uhlmann, J.K. 1997. A culminating advance in the theory and practice of data fusion, filtering, and decentralized estimation. Technical report. Covariance Intersection Working Group.http://www.ait.nrl.navy.mil/people/uhlmann/covlnt.html.

  • Wang, D. and Terman, D. 1997. Image segmentation based on oscilatory correlation. Neural Computation, 9:1623-1626.

    Google Scholar 

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Cozzi, A., Wörgötter, F. COMVIS: A Communication Framework for Computer Vision. International Journal of Computer Vision 41, 183–194 (2001). https://doi.org/10.1023/A:1011156004656

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  • DOI: https://doi.org/10.1023/A:1011156004656