Network Representations and Match Filters for Invariant Object Recognition

  • Harry Wechsler
Conference paper
Part of the NATO ASI Series book series (volume 30)


Artificial Intelligence (AI) deals with the types of problem solving and decision making that humans continuously face in dealing with the world. Such activity involves by its very nature complexity, uncertainty, and ambiguity which can “distort” the phenomena (e.g., imagery) under observation. However, following the human example, any artificial vision system should process information such that the results are invariant to the vagaries of the data acquisition process.


Computer Vision Image Representation Familiar Size Data Acquisition Process Visual Buffer 
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.


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  1. [1]
    Ballard, D.H., C.M. Brown (1982), “Computer Vision,” Prentice Hall, 1982.Google Scholar
  2. [2]
    Ballard, D.H., G.E. Hinton, T.J. Sejnowski, (1983), Parallel visual computation, “Nature,” 306, 5938, 21–26.CrossRefGoogle Scholar
  3. [3]
    Barrow, H.G., J.M. Tenenbaum (1978), Recovering intrinsic scene characteristics from images, in “Computer Vision Systems,” A. Hanson and E. Riseman (Eds.), Academic Press.Google Scholar
  4. [4]
    Brooks, R. (1981), Symbolic reasoning among 3-D models and 2-D images, “Artificial Intelligence,” 17, 1–3.CrossRefGoogle Scholar
  5. [5]
    Caulfield, H.J., M.H. Weinberg (1982), Computer recognition of 2-D pattern using generalized matched filters, “Applied Optics,” 21,9.CrossRefGoogle Scholar
  6. [6]
    Davis, L.S., A. Rosenfeld, (1981), Cooperating processes for low-level vision: a survey, “Artificial Intelligence,” 17, 1–3, 245–263.CrossRefGoogle Scholar
  7. [7]
    Edelman, G.M. (1982), Group selection and higher brain function, “Bulletin of the American Academy of Arts and Science,” vol. XXXVI, 1.Google Scholar
  8. [8]
    Farah, M.J. (1985), The neurological basis of mental imagery: a componential analysis, in “Visual Cognition,” S. Pinker (Ed.), MIT Press.Google Scholar
  9. [9]
    F la veil, J.H. (1985), “Cognitive Development,” (2nd Ed.), Prentice Hall.Google Scholar
  10. [10]
    Fukushima, K. (1984), A hierarchical neural network model for associative memory, “Biol. Cybernetics,” 50, 105–113.MATHCrossRefGoogle Scholar
  11. [11]
    Garvey, T.D., J.D. Lowrance (1983), Evidential reasoning: an implementation for multisensor integration, TN307, AI Center, SRI, Palo Alto, CA.Google Scholar
  12. [12]
    Granrud, C., R. Haake, A. Yonas (1985), Sensitivity to familiar size: the effects of memory on spatial perception, “Perception Psychophysics,” 37, 459–466.CrossRefGoogle Scholar
  13. [13]
    Hester, C., Casasent, D. (1981), Interclass discrimination using synthetic discriminant functions (SDF), “Proc. SPIE on Infrared Technology for Target Detection and Classification,” Vol. 302.Google Scholar
  14. [14]
    Hopfield, J.J. (1982), Neural networks and physical systems with emergent collective computational abilities, “Proc. Natl. Acad. Sci. USA,” 79, April 1982.Google Scholar
  15. [15]
    Hubel, D., Wiesel, T., (1979), Brain mechanisms of vision, “Scientific American “October.Google Scholar
  16. [16]
    Jacobson, L., H. Wechsler (1985a), Joint spatial/spatial frequency representations for image processing, “SPIE/Cambridge Int. Conference on Intelligent Robots and Computer Vision,” Boston, MA.Google Scholar
  17. [17]
    Jacobson, L., H. Wechsler (1985b), FOVEA — A system for invariant visual form recognition, “2nd Int. Symposium on Optical and Electro-Optical Applied Science and Recognition,” Cannes, France.Google Scholar
  18. [18]
    Kirpatrick, S., C.D. Gelatt, M.P. Vecchi (1983), Optimization by simulated annealing, “Science,” 220, 671.MathSciNetCrossRefGoogle Scholar
  19. [19]
    Kohonen, T. (1984), “Self-Organization and Associative-Memories,” Springer-Verlag.Google Scholar
  20. [20]
    Kuhn, T. (1970), “The Structure of Scientific Revolution,” The University of Chicago Press.Google Scholar
  21. [21]
    Minsky, M., S. Papert (1968), “Perceptrons,” MIT Press.Google Scholar
  22. [22]
    Mulgaonkar, P.G., L.G. Shapiro (1985), Hypothesis-based geometric reasoning about perspective images, “Proc. of the Third Workshop on Computer Vision, Representation and Control” Bellaire, Michigan.Google Scholar
  23. [23]
    Nahar, S., S. Sahni, E. Shragowitz (1985), Simulated annealing and combinatorial optimization, TR 85–56, Dept. of Computer Science, University of Minnesota.Google Scholar
  24. [24]
    Searl, J. (1985), “Mind, Brain and Science,” Harvard University Press.Google Scholar
  25. [25]
    Simon, H.A., (1984), “The Science of the Artificial,” (2nd ed.), MIT Press.Google Scholar
  26. [26]
    Tanimoto, S., T. Pavlidis (1975), A hierarchical data structure for picture processing, “Computer Graphics and Image Processing,” 4, 2, 104–119.CrossRefGoogle Scholar
  27. [27]
    Van Essen, D.C., J.H.R. Marniseli (1983), Hierarchical organization and functional streams in the visual cortex, TINS (Trends in Neuro Sciences).Google Scholar
  28. [28]
    Witkin, A.P., J.M. Tenenbaum (1983), On the role of structure in vision, in “Human and Machine Vision,” J. Beck, B. Hope and A. Rosenfeld (Eds.), Academic Press.Google Scholar
  29. [29]
    Wittgenstein, L. (1953), “Philosophical Investigations,” MacMillan.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

  • Harry Wechsler
    • 1
  1. 1.Department of Electrical EngineeringUniversity of MinnesotaMinneapolisUSA

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