Advertisement

Log-Map Analysis

  • Luca Lombardi
  • Marco Porta

Abstract

The image interpretation process is made up of a long sequence of steps: the image is sequentially stored, pre-processed and segmented, and finally after a feature extraction phase, the image content is analysed and interpreted or classified. This open loop paradigm does not support real-time processing, even for the simplest tasks that humans perform without effort.

Keywords

Connectivity Graph Camera Sensor Computer Vision System Laplacian Pyramid Indoor Scene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

REFERENCES

  1. 1.
    P. J. Burt, ‘Smart Sensing’ in Machine Vision, in Machine Vision: Algorithms, Architectures and Systems, H. Freeman, Ed., San Diego, CA: Academic Press, 1:30 (1988).Google Scholar
  2. 2.
    M. G. Albanesi, V. Cantoni, U. Cei, M. Ferretti, and M. Mosconi, Embedding Pyramids into Mesh Arrays, in: Reconflgurable Massively Parallel Computers, H. Li, and Q. F. Stout, eds., Englewood Cliffs: Prentice Hall, 123:140 (1991).Google Scholar
  3. 3.
    P. J. Burt, Attention Mechanisms for Vision in a Dynamic World, Proc. 9th Int. Conf. on Pattern Recognition, Rome, 977:987 (1988).Google Scholar
  4. 4.
    P. J. Burt, C. H. Anderson, J. O. Sinniger, and G. van der Wal, A Pipeline Pyramid Machine, in Pyramidal Systems for Computer Vision, V. Cantoni and S. Levialdi eds. Berlin, FRG: Springer-Verlag, 133:152 (1986).Google Scholar
  5. 5.
    V. Cantoni, I. P. Hierarchical Systems: Architectural Features, in: Pyramidal Systems for Computer Vision, V. Cantoni, S. Levialdi, eds., Berlin: Springer, 73:87 (1986).Google Scholar
  6. 6.
    E. L. Schwartz, Computational Anatomy and Functional Architecture of Striate Cortex: A Spatial Mapping Approach to Perceptual Coding, Vision Research, Vol. 20, 645: (1980).CrossRefGoogle Scholar
  7. 7.
    Y. Yeshurum, and E. L. Schwartz: Shape Description with a Space Variant Sensor: Algorithms for Scan-path, Fusion, and Convergence over Multiple Scans, IEEE Trans. on PAMI, Vol. 11, 1217:1222 (1989).CrossRefGoogle Scholar
  8. 8.
    Benjamin B. Bendenson, Richard S. Wallace, and Eric L. Schwartz: A miniaturized active vision system, Proc. 11th Int. Conf. on Pattern Recognition, The Hague, NL, 58:614 (1992).Google Scholar
  9. 9.
    A. Rosenfeld, ed., Multiresolution Image Processing, Berlin, FRG: Springer Verlag, 1984.MATHCrossRefGoogle Scholar
  10. 10.
    T. Baron, M. D. Levine, and Y. Yeshurum, Exploring with a foveated robot eye system, Proc. 12th Int. Conf. on Pattern Recognition, vol. d, 377:380, (1994).Google Scholar
  11. 11.
    C. R. Dyer, Multiscale Image Understanding, in: Parallel Computer Vision, L. Uhr, ed., Orlando, FL, Academic Press, 171:213 (1987).Google Scholar
  12. 12.
    Alan S. Rojer, and Eric L. Schwartz: Design Considerations for a Space-Variant Visual Sensor with Complex-Logarithmic Geometry, Proc. 10th Int. Conf. on Pattern Recognition, Atlantic City, NJ, 278:285, (1990).Google Scholar
  13. 13.
    Fernando Pardo, Isaac Llorens, Francisco Micò, and Josè a. Boluda, Space variant vision and pipelined architecture for time to impact computation, Proc. CAMP 2000, Virginio Cantoni and Concettina Guerra eds., IEEE Comp. Society, 122:126 (2000).Google Scholar
  14. 14.
    F. Jurie, A new log-polar mapping for space variant imaging. Application to face detection and tracking, Pattern Recognition, Vol. 32, Pergamon, 865:875 (1999).Google Scholar
  15. Andrea Broglia, L’uso delle mappe polari-logaritmiche nella visione 3D, thesis, Pavia, (1997).Google Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Luca Lombardi
    • 1
  • Marco Porta
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaPaviaItaly

Personalised recommendations