Modeling uncertainty in human perception

  • Panos A. Ligomenides
Section III Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 286)


Human perception of resemblance in spatio-temporal patterns is modeled with the procedural normal description schemata [5–11], which are used for gathering experiential knowledge embedded in sensory data. The representation and the quantification of uncertainty in human perception and in the formal schemata which model it are examined in this paper.


Fuzzy Number Human Perception Internal Model Semantic Content Fuzzy Subset 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981Google Scholar
  2. [2]
    K.S. Fu, "Recent Progress in Syntactic Pattern Recognition", in Progress in Pattern Recognition, L.N. Kanal and A. Rosenfeld (Eds), North Holland, 1981Google Scholar
  3. [3]
    K. Fugunaga, Introduction to Statistical Pattern Recognition, Academic Press, New York, 1972Google Scholar
  4. [4]
    K.S. Lashley, "The Problem of Serial Order in Behavior", in Celelial Mechanisms of Behavior, L.A. Jefress (Ed), Wiley, New York, 1951Google Scholar
  5. [5]
    P.A. Ligomenides, "The Experiential Knowledge Base as a Cognitive Prosthesis", in Visual Languages, S.K. Chang, T. Ichikawa and P.A. Ligomenides (Eds), Plenum Press, New York, 1986Google Scholar
  6. [6]
    P.A. Ligomenides, "On-line recognition and labeling of behavioral modalities", Proc. IEEE Conf. on Systems, Man and Cybernetics, Atlanta, Ga, Oct.14–17, 1986Google Scholar
  7. [7]
    P.A. Ligomenides, "Fuzzy conformity measures for experiential knowledge representation", Proc. NAFIPS Conference, Purdue Univ., May 5–7, 1987Google Scholar
  8. [8]
    P.A. Ligomenides, "Resemblance, fuzziness and uncertainty in experiential knowledge engineering", Proc. Int'l Symp. on Fuzzy Sys. & Knowl. Eng., Guangzhou, China, July 10–16, 1987Google Scholar
  9. [9]
    P.A. Ligomenides, "Real-time semantic imaging of decision-making worlds", Proc. IEEE Workshop on Languages for Automation, Vienna, Austria, Aug. 24–27, 1987Google Scholar
  10. [10]
    P.A. Ligomenides, "Models of Human Progressive Perception of Reseamblance for the Experiential Knowledge Base", to appearGoogle Scholar
  11. [11]
    D. Panagiotopoulos, Computer-Based Models of Human Perception of Behavioral Patterns, PhD Thesis, EE Dept, University of Maryland (expected)Google Scholar
  12. [12]
    T. Pavlides, Structural Pattern Recognition, Springer Verlag, New York, 1977Google Scholar
  13. [13]
    Ju.A. Schreider, Equality, Resemblance and Order, Mir Publ., Moscow, 1975Google Scholar
  14. [14]
    R.R. Yager, "Linguistic Representation of Default Values in Frames", IEEE Trans. on Systems, Man and Cybernetics, SMC-14,4, July/Aug. 1984Google Scholar
  15. [15]
    K.C. Yau and K.S. Fu, "Syntactic shape recognition using attributed grammars", Proc. 8th Annual EIA Symp. on Automat. Imagery Pattern Recognition, 1978Google Scholar
  16. [16]
    L.A. Zadeh, "A Computational Approach to Fuzzy Quantifiers in Natural Languages", Comp. & Math. with Applications, vol. 9, pp.149–184, 1983Google Scholar
  17. [17]
    M. Zeleny, Multiple Criteria Decision Making, McGraw-Hill, New York, 1982Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

  • Panos A. Ligomenides
    • 1
  1. 1.Cybernetics Research Laboratory E.E. DeptUniversity of MarylandCollege ParkUSA

Personalised recommendations