An interactive constraint-based system for selective attention in visual search

  • R. Cucchiara
  • E. Lamma
  • P. Mello
  • M. Milano
Communications Session 5B Intelligent Information Systems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1325)


In this paper, we face the problem of model-based object recognition in a scene. Computer vision techniques usually separate the extraction of visual information from the scene from the reasoning on the symbolic data. We propose to interactively intertwine the two parts: the reasoning task on visual information is based on constraint satisfaction techniques. Objects are modeled by means of constraints and constraint propagation recognizes an object in the scene. To this purpose, we extend the classical Constraint Satisfaction Problem (CSP) approach which is not suitable for coping with undefined information. We thus propose an Interactive CSP model for reasoning on partially defined data, generating new constraints which can be used to guide the search and to incrementally process newly acquired knowledge.


Computer Vision Constraint Satisfaction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    V. Cantoni, “Attentional Engagement in vision system”, in Artificial Vision Academic Press 1997.Google Scholar
  2. 2.
    P. N. Rao, D.H.Ballard, “An active vision architecture based on iconic representations”, Artificial Intelligence, 78, pp. 461–505, 1995.Google Scholar
  3. 3.
    J. Lemaire, “Use of a priori descriptions in a high level language and management of the uncertainty in a scene recognition system”, Proc. of ICPR96, vol.1, pp 560–564, IEEE Press, 1996.Google Scholar
  4. 4.
    M. Bolle, A. Califano, R. Kjeldsen “A complete and extendible approach to visual recognition”, IEEE Trans. on PAMI, 14, 5, 1992.Google Scholar
  5. 5.
    P.Gaston, T. Lozano Peres, “Tactile recognition and localization using object models: the case of polyhedra on a plane”, IEEE Trans. on PAMI, 6, 3, 257–265, 1984.Google Scholar
  6. 6.
    I.D. Reid, J. M. Brady, “Recognition of object classes from range data” Artificial Intelligence, 78, pp. 289–365, 1995.Google Scholar
  7. 7.
    P.Pellegretti, F. Roli, S. Serpico, G. Vernazza, “Supervised learning of descriptions for image recognition purposes”, IEEE Trans. on PAMI, vol. 16 n. 1, pp. 92–98 (1994).Google Scholar
  8. 8.
    B. Draper, A. Hanson, E.Riseman “Knowledge-directed vision: control, learning and integration”, Proc. of IEEE, vol. 84, n. 11, pp. 1625–1681, 1996.Google Scholar
  9. 9.
    R. Baicsy “Active Perception”, Proc. of IEEE, vol. 76 n. 8, pp. 996–1005, 1988.Google Scholar
  10. 10.
    V. Kumar, “Algorithms for Constraint-Satisfaction Problems: A Survey”, in AI Magazine, vol. 13, 1992, pp. 32–44.Google Scholar
  11. 11.
    J.F. Puget, “On the Satisfiability of Symmetrical Constrained Satisfaction Problems”, Tech. Report ILOG Headquarters, 1993.Google Scholar
  12. 12.
    P.Van Hentenryck, “Constraint Satisfaction in Logic Programming”, MIT Press, 1989.Google Scholar
  13. 13.
    D. Vernon, “Machine Vision: Automated Visual Inspection and Robot Vision” Prentice Hall, 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • R. Cucchiara
    • 2
  • E. Lamma
    • 1
  • P. Mello
    • 2
  • M. Milano
    • 1
  1. 1.DEIS, Univ. BolognaBolognaItaly
  2. 2.Dip. IngegneriaUniv. Ferrara, Via SaragatFerraraItaly

Personalised recommendations