Advertisement

Cognitive Vision and Perceptual Grouping by Production Systems with Blackboard Control – An Example for High-Resolution SAR-Images

  • Eckart Michaelsen
  • Wolfgang Middelmann
  • Uwe Sörgel
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 4)

Abstract

The laws of gestalt-perception play an important role in human vision. Psychological studies identified similarity, good continuation, proximity and symmetry as important inter-object relations that distinguish perceptive gestalts from arbitrary sets of clutter objects. Particularly, symmetry and continuation possess a high potential in detection, identification, and reconstruction of man-made objects. This contribution focuses on coding this principle in an automatic production system. Such systems capture declarative knowledge. Procedural details are defined as control strategy for an interpreter. Often an exact solution is not feasible while approximately correct interpretations of the data with the production system are sufficient. Given input data and a production system the control acts accumulatively instead of reducing. The approach is assessment driven features any-time capability and fits well into the recently discussed paradigms of cognitive vision. An example from automatic extraction of groupings and symmetry in man-made structure from high resolution SAR-image data is given. The contribution also discusses the relations of such approach to the “mid-level” of what is today proposed as “cognitive vision”.

Keywords

Cognitive vision Perceptual grouping Production systems Blackboard control SAR images 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cohn, A.G., Magee, D., Galata, A., Hogg, D., Hazarika, S.: Towards an architecture for cognitive vision using qualitative spatio-temporal representations and abduction. In: Freksa, C., Brauer, W., Habel, C., Wender, K.F. (eds.) Spatial Cognition III. LNCS (LNAI), vol. 2685, pp. 232–248. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. Ender, J.H.G., Brenner, A.R.: PAMIR - a wideband phased array SAR/MTI system. IEE Proceedings - Radar, Sonar, Navigation 150(3), 165–172 (2003)CrossRefGoogle Scholar
  3. Foerstner, W.: A framework for low level feature extraction. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 383–394. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  4. Guo, C.-E., Zhu, S.C., Wu, Y.N.: Modelling visual patterns by integrating descriptive and generative methods. IJCV 53(1), 5–29 (2003)CrossRefGoogle Scholar
  5. Draper, B., Collins, R., Brolio, J., Hanson, A., Riseman, E.: The Schema System. IJCV 2, 209–250 (1989)CrossRefGoogle Scholar
  6. Klausing, H., Holpp, W.: Radar mit realer und synthetischer Apertur, Oldenburg Verlag, München (2000)Google Scholar
  7. Laptev, I., Mayer, H., Lindeberg, T., Eckstein, W., Steger, C., Baumgartner, A.: Automatic Extraction of Roads from Aerial Images Based on Scale Space and Snakes. Machine Vision and Applications 12(1), 22–31 (2000)CrossRefGoogle Scholar
  8. Leavers, V.F.: Which Hough transform? CVGIP, Image Understanding, Vol. CVGIP, Image Understanding 58(2), 250–264 (1993)CrossRefGoogle Scholar
  9. Lowe, D.G.: Perceptual organization and visual recognition. Kluwer, Boston (1985)CrossRefGoogle Scholar
  10. Marr, D.: Vision. Freeman, San Francisco (1982)Google Scholar
  11. Matsuyama, T., Hwang, V.S.-S.: Sigma a knowledge-based image understanding system. Plenum Press, New York (1990)Google Scholar
  12. Medioni, G., Lee, M., Tang, C.: A computational framework for segmentation and grouping. Elsevier, Amsterdam (2000)zbMATHGoogle Scholar
  13. Michaelsen, E.: Über Koordinaten Grammatiken zur Bildverarbeitung und Szenenanalyse. Diss. Univ. of Erlangen (1998a), available online as http://www.exemichaelsen.de/Michaelsen_Diss.pdf
  14. Michaelsen, E., Stilla, U.: Remark on the notation of coordinate grammers. In: Armin, A., Dori, D., Pudil, P., Freeman, H. (eds.) Advances in pattern recognition, JOINT IAPR Int. Workshop SPR-SSPR, pp. 421–428. Springer, Berlin (1998b)Google Scholar
  15. Michaelsen, E., Stilla, U.: Probabilistic Decisions in Production Nets: An Example from Vehicle Recognition. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 225–233. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. Michaelsen, E., Soergel, U., Stilla, U.: Grouping salient scatterers in InSAR data for recognition of industrial buildings. In: Kasturi, R., Laurendeau, D., Sun, C. (eds.) ICPR 2002. 16th Int. Conf. on Pattern Recognition, vol. II, pp. 613–616 (2002)Google Scholar
  17. Michaelsen, E., Middelmann, W., Sörgel, U., Thönnessen, U.: On the improvement of structural detection of building features in high-resolution SAR data by edge preserving image enhancement. Pattern Recognition and Image Analysis, MAIK, NAUKA, Moscow 15(4), 686–689 (2005)Google Scholar
  18. Stilla, U., Michaelsen, E., Lütjen, K.: Automatic Extraction of Buildings from Aerial Images. In: F. Leberl, R. Kalliany, M. Gruber (eds.), Mapping Buildings, Roads and other Man-made Structures from Images, IAPR-TC7, Wien, Oldenburg, pp. 229-244 (1996)Google Scholar
  19. Wertheimer, M.: Untersuchungen zur Lehre von der Gestalt II. Psychol. Forsch. In: Beardslee, D., Wertheimer, M. (eds.) Translated as Principles of Perceptual Organization, vol. 4, pp. 115–135. Princeton, NJ (1923)Google Scholar
  20. ECVision: European research network for cognitive vision systems, A research roadmap of cognitive vision (2005), http://www.ecvision.org

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Eckart Michaelsen
    • 1
  • Wolfgang Middelmann
    • 1
  • Uwe Sörgel
    • 2
  1. 1.FGAN-FOM, Gutleuthausstrasse 1, 76275 EttlingenGermany
  2. 2.Institute of Photogrammetry and GeoInformation, University of Hanover, Nienburger Satrasse 1, 30167 HannoverGermany

Personalised recommendations