Advertisement

An Attentional Approach for Perceptual Grouping of Spatially Distributed Patterns

  • Muhammad Zaheer Aziz
  • Bärbel Mertsching
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4713)

Abstract

A natural (human) eye can easily detect large visual patterns or objects emerging from spatially distributed discrete entities. This aspect of pattern analysis has been barely addressed in literature. We propose a biologically inspired approach derived from the concept of visual attention to associate together the distributed pieces of macro level patterns. In contrast to the usual approach practiced by the existing models of visual attention, this paper introduces a short-term excitation on the features and locations related to the current focus of attention in parallel to the spatial inhibition of return. This causes the attention system to fixate on analogous units in the scene that may formulate a meaningful global pattern. It is evident from the results of experiments that the outcome of this process can help in widening the scope of intelligent machine vision.

Keywords

Visual Attention JPEG Compression Perceptual Grouping Illusory Contour Attentional Approach 
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.
    Wertheimer, M.: Laws of organization in perceptual forms. In: Ellis, W.D. (ed.) A Source Book of Gestalt Psychology, pp. 71–88 (1938)Google Scholar
  2. 2.
    Ogmen, H., Otto, T., Herzog, M.H.: Perceptual grouping induces non-retinotopic feature attribution in human vision. Vision Research 46, 3234–3242 (2006)CrossRefGoogle Scholar
  3. 3.
    Julesz, B.: Textons, the elements of texture perception, and their interactions. Nature, 91–97 (1981)Google Scholar
  4. 4.
    Glass, L.: Moire effect from random dots. Nature 223, 578–580 (1969)CrossRefGoogle Scholar
  5. 5.
    Ahuja, N., Tuceryan, M.: Extraction of early perceptual structure in dot patterns: Integrating region, boundary, and component gestalt. Computer Vision, Graphics, and Image Processing 48, 304–356 (1989)CrossRefGoogle Scholar
  6. 6.
    Datta, A., Parui, S., Chaudhuri, B.: Skeletal shape extraction from dot patterns by self-organization. In: 13th ICPR, vol. 4, pp. 80–84 (1996)Google Scholar
  7. 7.
    Stevens, K.A., Brookes, A.: Detecting structure by symbolic constructions on tokens. Computer Vision, Graphics, and Image Processing 37, 238–260 (1987)CrossRefGoogle Scholar
  8. 8.
    Hund, M., Mertsching, B.: A Computational Approach to Illusory Contour Perception Based on the Tensor Voting Technique. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Ackermann, F., Massmann, A., Posch, S., Sagerer, G., Schueter, D.: Perceptual grouping of contour segements using markov random fields. Pattern Recognition and Image Analysis 7, 11–17 (1997)Google Scholar
  10. 10.
    Peters, R.J., Iyer, A., Itti, L., Koch, C.: Components of bottom-up gaze allocation in natural images. Vision Research 45, 2397–2416 (2005)CrossRefGoogle Scholar
  11. 11.
    Carreira, M.J., Orwell, J., Turnes, R., Boyce, J.F.: Perceptual grouping from gabor filter responses. In: BMVC 1998, Southampton - UK (1998)Google Scholar
  12. 12.
    Itti, L., Koch, U., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on PAMI 20, 1254–1259 (1998)Google Scholar
  13. 13.
    Itti, L., Koch, C.: A saliency based search mechanism for overt and covert shifts of visual attention. Vision Research, 1489–1506 (2000)Google Scholar
  14. 14.
    Navalpakkam, V., Itti, L.: Modeling the influence of task on attention. Vision Research, 205–231 (2005)Google Scholar
  15. 15.
    Navalpakkam, V., Itti, L.: Top-down attention selection is fine-grained. Journal of Vision 6, 1180–1193 (2006)CrossRefGoogle Scholar
  16. 16.
    Rothenstein, A., Tsotsos, J.: Selective tuning: Feature binding through selective attention. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4132, pp. 548–557. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Park, S.J., Ban, S.J., Sang, S.W., Shin, J.K., Lee, M.: Implementation of visual attention system using bottom-up saliency map model. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 678–685. Springer, Heidelberg (2003)Google Scholar
  18. 18.
    Meur, O.L., Callet, P.L., Barba, D., Thoreau, D.: A coherent computational approach to model bottom-up visual attention. IEEE Transactions on PAMI 28, 802–817 (2006)Google Scholar
  19. 19.
    Sun, Y., Fischer, R.: Object-based visual attention for computer vision. Artificial Intelligence 146, 77–123 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Backer, G., Mertsching, B., Bollmann, M.: Data- and model-driven gaze control for an active-vision system. IEEE Transactions on PAMI 23, 1415–1429 (2001)Google Scholar
  21. 21.
    Frintrop, S., Backer, G., Rome, E.: Goal-directed search with a top-down modulated computational attention system. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) Pattern Recognition. LNCS, vol. 3663, pp. 117–124. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  22. 22.
    Aziz, M.Z., Mertsching, B., Shafik, M.S., Stemmer, R.: Evaluation of visual attention models for robots. In: ICVS 2006, New York - USA. index–20, IEEE, Los Alamitos (2006)Google Scholar
  23. 23.
    Aziz, M.Z., Mertsching, B.: Color saliency and inhibition in region based visual attention. In: WAPCV 2007, Hyderabad - India, pp. 95–108 (2007)Google Scholar
  24. 24.
    Aziz, M.Z., Mertsching, B.: Pop-out and IOR in static scenes with region based visual attention. In: WCAA-ICVS 2007, Bielefeld - Germany, Bielefeld University eCollections (2007)Google Scholar
  25. 25.
    Aziz, M.Z., Mertsching, B.: Color segmentation for a region-based attention model. In: Farbworkshop 2006, Ilmenau - Germany, pp. 74–83 (2006)Google Scholar
  26. 26.
    Goolsby, B.A., Grabowecky, M., Suzuki, S.: Adaptive modulation of color salience contingent upon global form coding and task relevance. Vision Research, 901–930 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Muhammad Zaheer Aziz
    • 1
  • Bärbel Mertsching
    • 1
  1. 1.GET Lab, University of Paderborn, Pohlweg 47-49, 33098 PaderbornGermany

Personalised recommendations