Reflections on Cognitive Vision Systems

  • Hans-Hellmut Nagel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2626)


A long list of buzzwords which percolated through the computer vision community during the past thirty years leads to the question: does ‘Cognitive Vision Systems’ just denote another such ‘fleeting fad’? Upon closer inspection, many apparent ‘buzzwords’ refer to aspects of computer vision systems which became a legitimate target of widespread research interest due to methodological advances or improvements of computer technology. Following a period during which particular topics had been investigated intensively, associated results merged into the general pool of commonly accepted methods and tools, their preponderance faded and time appeared ‘ripe again for the next buzzword’. Such a non-pejorative use of buzzword in the sense of ‘focus of research attention’ appears appropriate, too, for cognitive vision.

It will be argued that cognitive vision could be characterized by a systematic attempt to conceive and implement computer vision systems based on multiple variably-connected, multi-scale consistency requirements extending beyond the domain of signal and geometric processing into the domain of conceptual representations. This in turn necessitates that methods of formal logic will have to be incorporated into computer vision systems. As a consequence, knowledge has to be explicated in order to facilitate its exploitation in many different contexts.


System aspects consistency requirements conceptual system levels integration of geometric and conceptual aspects integration of inference engines into vision 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    M. Campbell, A.J. Hoane Jr., F.-H. Hsu: Deep Blue. AIJ 134 (2002) 57–83.zbMATHGoogle Scholar
  2. 2.
    M. Gazzaniga (Ed.): The Cognitive Neurosciences. MIT Press: Cambridge 1995.Google Scholar
  3. 3.
    R. Gerber, H.-H. Nagel, and H. Schreiber: Deriving Textual Descriptions of Road Traffic Queues from Video Sequences. In: F. van Harmelen (Ed.): Proc. ECAI-2002, 21–26 July 2002, Lyon, France. IOS Press: Amsterdam 2002, pp. 736–740.Google Scholar
  4. 4.
    M. Haag und H.-H. Nagel: ‘Begriffliche Rückkopplung’ zur Behandlung von Verdeckungssituationen in der Bildfolgenauswertung von Straßenverkehrsszenen. In J. Dassow, R. Kruse (Hrsg.), Informatik’ 98 — Informatik zwischen Bild und Sprache, Springer-Verlag Berlin·Heidelberg 1998, pp. 13–22 (in German).Google Scholar
  5. 5.
    M. Haag and H.-H. Nagel: Combination of Edge Element and Optical Flow Estimates for 3D-Model-Based Vehicle Tracking in Traffic Image Sequences. International Journal of Computer Vision 35:3 (1999) 295–319.CrossRefGoogle Scholar
  6. 6.
    R.J. Howarth and H. Buxton: Conceptual Descriptions from Monitoring and Watching Image Sequences. Image and Vision Computing 18:2 (2000) 105–135.CrossRefGoogle Scholar
  7. 7.
    St. Intille and A. Bobick: Visual Recognition of Multi-Agent Action Using Binary Temporal Relations. In Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR’99), 23–25 June 1999, Fort Collins, Colorado, Vol. 1, pp. 56–62.Google Scholar
  8. 8.
    H. Leuck and H.-H. Nagel: Model-Based Initialisation of Vehicle Tracking: Dependency on Illumination. Proc. 8th Intern. Conf. Computer Vision, 9–12 July 2001, Vancouver/BC, Canada, Vol. I, pp. 309–314. IEEE CS: Los Alamitos/CA.Google Scholar
  9. 9.
    Z.-Q. Liu, L.T. Bruton, J.C. Bezdek, J.M. Keller, S. Dance, N.R. Bartley, and C. Zhang: Dynamic Image Sequence Analysis Using Fuzzy Measures. IEEE Trans. Systems, Man, and Cybernetics-Part B 31:4 (2001) 557–572.CrossRefGoogle Scholar
  10. 10.
    J. Pearl: Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley Publ. Co.: Reading, MA 1984.Google Scholar
  11. 11.
    P. Remagnino, T. Tan, and K. Baker: Agent Oriented Annotation in Model Based Visual Surveillance. Sixth ICCV, 4–7 January 1998, Bombay, India, pp. 857–862.Google Scholar
  12. 12.
    S. Russell and P. Norvig: Artificial Intelligence — A Modern Approach. Prentice-Hall, Inc.: Upper Saddle River, NJ 1995.zbMATHGoogle Scholar
  13. 13.
    J. Schaeffer and H. Jaap van den Herik: Games, Computers, and Artificial Intelligence. Artificial Intelligence Journal (AIJ) 134 (2002) 1–7.zbMATHCrossRefGoogle Scholar
  14. 14.
    M. Spengler and B. Schiele: Multi-Object Tracking: Explicit Knowledge Representation and Implementation for Complexity Reduction. Proc. Workshop on Cognitive Vision, 19–20 September 2002, Zurich, CH, pp. 9–16.Google Scholar
  15. 15.
    T.N. Tan, G.D. Sullivan, and K.D. Baker: Model-Based Localisation and Recognition of Road Vehicles. Intern. Journal of Computer Vision 27:1 (1998) 5–25.CrossRefGoogle Scholar
  16. 16.
    R.A. Wilson: The Cognitive Sciences: A Comment on 6 Reviews of ‘The MIT Encyclopedia of the Cognitive Sciences’. Artif. Intelligence 130:2 (2001) 223–229.CrossRefGoogle Scholar
  17. 17.
    R.A. Wilson and F.C. Keil (Eds.): The MIT Encyclopedia of the Cognitive Sciences. The MIT Press: Cambridge, MA 1999.Google Scholar
  18. 18.
    Proc. IEEE Intelligent Vehicles Symposium, 18–20 June 2002, Versailles/FranceGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hans-Hellmut Nagel
    • 1
  1. 1.Institut für Algorithmen und Kognitive SystemeUniversität Karlsruhe (TH)KarlsruheGermany

Personalised recommendations