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LOW RESOLUTION IMAGE SAMPLING FOR PATTERN MATCHING

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Computer Vision and Graphics

Part of the book series: Computational Imaging and Vision ((CIVI,volume 32))

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Abstract

The paper presents a simulated mobile system that learns to solve the egolocation task in a known environment, in a supervised way, using a very low resolution sampling of the optical array and RBF approximation techniques. The impact of the number of sensors, of their layout, in particular of Sobol sequences with respect to regular grids for a progressively refined sampling of images, and of the complexity of response of each sensing unit has been investigated in an attempt to simplify as much as possible the architecture of the image processing module retaining good localization ability.

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REFERENCES

  • Bertamini, F., Brunelli, R., Lanz, O., Roat, A., Santuari, A., Tobia, F., and Xu, Q. (2003). Olympus: an ambient intelligence architecture on the verge of reality. In Proc. of ICIAP 2003, pages 232–237, Mantova, Italy.

    Google Scholar 

  • Brunelli, R. and Mich, O. (2000). Image Retrieval by Examples. IEEE Transactions on Multimedia, 2(3):164–171.

    Article  Google Scholar 

  • Freeman, W. T. and Adelson, E. H. (1991). The design and use of steerable filter. IEEE Transactions on PAMI, 13(9):891–906.

    Google Scholar 

  • Poggio, T., Fahle, M., and Edelman, S. (1992). Fast perceptual learning in visual hyperacuity. Science, 247:1018–1021.

    Google Scholar 

  • Poggio, T. and Girosi, F. (1990). Regularization algorithms for learning that are equivalent to multilayer networks. Science, 247:978–982.

    MathSciNet  Google Scholar 

  • Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. (1992). Numerical Recipes in C. Cambridge University Press, 2nd edition.

    Google Scholar 

  • Schiele, B. and Crowley, J. L. (2000). Recognition without correspondence using multidimensional receptive field histograms. International Journal of Computer Vision, 36(1):31–50.

    Article  Google Scholar 

  • Snippe, J. and Koenderink, J. J. (1992). Discrimination thresholds for channel-coded systems. Biological Cybernetics, 66:543–551.

    Article  Google Scholar 

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© 2006 Springer

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Brunelli, R. (2006). LOW RESOLUTION IMAGE SAMPLING FOR PATTERN MATCHING. In: Wojciechowski, K., Smolka, B., Palus, H., Kozera, R., Skarbek, W., Noakes, L. (eds) Computer Vision and Graphics. Computational Imaging and Vision, vol 32. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4179-9_140

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  • DOI: https://doi.org/10.1007/1-4020-4179-9_140

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-4178-5

  • Online ISBN: 978-1-4020-4179-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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