Advertisement

Visual Attention Mechanisms in Motion Analysis

  • Vito Roberto

Abstract

Attention mechanisms are ubiquitous in machine vision: they underlie the multiple control layers of a system, providing a wide range of strategies, algorithms and heuristics to solve real-world problems.

Keywords

Root Mean Square Machine Vision Attention Mechanism Active Vision Interest Operator 
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.
    D.Sagi, contribution to the present volume.Google Scholar
  2. 2.
    S.L.Tanimoto, Allocation of attention in vision, in: Human and Machine Vision: Analogies and Divergencies, V.Cantoni ed., Plenum Press, New York, p.171 (1994).Google Scholar
  3. 3.
    H.P.Moravec, Toward automatic visual obstacle avoidance, in: Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI), p.584 (1977).Google Scholar
  4. 4.
    C.G.Harris and M.Stephens, A combined corner and edge detector, in;Proc. 4th Alvey Vision Conf., Manchester, UK, 189:192 (1988).Google Scholar
  5. 5.
    J.A.Noble, Finding corners, Image and Vision Computing, vol.6, 121:128 (1988).CrossRefGoogle Scholar
  6. 6.
    http://www.vasc.ri.cmu.edu/idb/html/motion/index.htmlGoogle Scholar
  7. 7.
    E.Trucco and A.Verri, Introductory Techniques for 3D Computer Vision, Prentice Hall (1998).Google Scholar
  8. 8.
    C.Tomasi and T.Kanade, Detection and tracking of point features. Technical Report CMU-CS-91132, Carnegie-Mellon Univ. (1991).Google Scholar
  9. 9.
    J.Shi and C.Tomasi, Good features to track. In; Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 593:600 (1994).Google Scholar
  10. 10.
    I.J.Cox, S.Roy, and S.L.Hingorani. Dynamic histogram warping of image pairs from constant image brightness, IEEE International Conf. on Image Processing (ICIP), vol.2, 366:369 (1995).Google Scholar
  11. 11.
    G.D.Hager and P.N.Belhumeur, Efficient region tracking with parametric models of geometry and illumination, IEEE Trans. Patt. Analysis and Machine Intelligence (PAMI), 20 (10) 1025:1039 (1998).CrossRefGoogle Scholar
  12. 12.
    T.Tommasini, A.Fusiello, E.Trucco and V.Roberto, Making good features track better, in: Proc. IEEE CVPR, 178:181 (1998).Google Scholar
  13. 13.
    F.R.Hampel, P.J.Rousseeuw, E.Ronchetti and W.Stahel, Robust Statistics: the Approach Based on Influence Functions, John Wiley & Sons (1986).Google Scholar
  14. 14.
    P.B.Yale, Geometry and Symmetry, Dover (1968).Google Scholar
  15. 15.
    A.Censi, A.Fusiello and V.Roberto, Image stabilization by features tracking, in; Proc. of the 10th Int. Conf. on Image Analysis and Processing (ICIAP99), Venice, IEEE Comp. Soc., Los Alamitos, CA, 665:668 (1999).Google Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Vito Roberto
    • 1
  1. 1.Machine Vision LaboratoryUniversity of UdineItaly

Personalised recommendations