• Heinrich Niemann
Part of the Springer Series in Information Sciences book series (SSINF, volume 4)


Having discussed a variety of methods for preprocessing, extraction of simple constituents, and classification we proceeded with considering some details of representation and manipulation of data, in particular data which are results of the aforementioned steps of processing. Now we turn to a discussion of some general principles for the choice of processing methods and the order of their application to subsets of competing intermediate results; specification of these steps is referred to as control A distinct control module is introduced into a pattern analysis system in order to allow a flexible system structure as indicated in Sect. 1.4. It became apparent that in analysis of (complex) patterns a module should be at hand which makes the best possible use of available processing methods for every pattern f r (x) ε Ω. One sequence of methods or processing steps, which is suited for a particular pattern f r (x), need not be optimal for another pattern f s (x). The control module should be able to find this optimal sequence, or at least a fairly good sequence of processing steps depending on the pattern offered to the system.


Search Tree Optimal Path Start Node Edge Cost Basic Probability Assignment 
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. 6.1
    N.J. Nilsson: Problem Solving Methods in Arificial Intelligence (McGraw-Hill, New York 1971)Google Scholar
  2. 6.2
    H. Niemann: Control Strategies in Image and Speech Understanding. Proc. GWAI, Informatik Fachberichte, Vol.76 (Springer, Berlin, Heidelberg 1983) pp.31–49Google Scholar
  3. 6.3
    H. Niemann, H. Bunke: Künstliche Intelligenz in Bild- und Sprachanalyse (Teubner, Stuttgart 1987)CrossRefGoogle Scholar
  4. 6.4
    V.R. Lesser, R.D. Fennell, R.D. Erman, D.R. Reddy: Organisation of the HEARSAY II speech understanding system. IEEE Trans. ASSP-23, 11 (1975)Google Scholar
  5. 6.5
    B.T. Lowerre: The Harpy Speech Recognition System. PhD Thesis, Dept. Comput. Sei., Carnegie-Mellon Univ., Pittsburgh, PA (1976)Google Scholar
  6. 6.6
    C.A. Harlow: Image analysis and graphs. Comp. Graphics and Image Processing 2, 60 (1973)CrossRefGoogle Scholar
  7. 6.7
    Z. Manna: Mathematical Theory oj Computation (McGraw Hill, New York 1974)Google Scholar
  8. 6.8
    T.W. Pratt: Hierarchical graph model of the semantics of programs. Proc. AFIPS, Summer Joint Conf. (1969) pp.813–825Google Scholar
  9. 6.9
    C.A. Petri: Communication with automata. Suppl.1, Tech. Rep. RAD C-TR-65-337, Vol.1, Griffis Air Force Base, New York (1966) [transi, from Kommunikation mit Automaten, Univ. Bonn, Germany 1962]Google Scholar
  10. 6.10
    A.C. Shaw: Parsing of graph-representable pictures. J. Assoc. Comput. Mach. 17, 453 (1970)MATHCrossRefGoogle Scholar
  11. 6.11
    C. Hewitt: Viewing control structures as patterns of passing messages. Artif. Intell. 8, 323 (1977)CrossRefGoogle Scholar
  12. 6.12
    M. Georgeff: A framework for control in production systems. Rpt. No. STAN-CS-79-716, Comp. Science Dept., Stanford Univ. (1979)Google Scholar
  13. 6.13
    H. Prade: A computational approach to approximate and plausible reasoning with applications to expert systems. IEEE Trans. PAMI-7, 260 (1985)Google Scholar
  14. 6.14
    L.A. Zadeh: Fuzzy logic. Computer 21, No.4, 83 (1988)CrossRefGoogle Scholar
  15. 6.15
    R. De Mori, L. Saitta: Automatic learning of fuzzy naming relations over finite languages. Information Sci. 20, 93 (1980)CrossRefGoogle Scholar
  16. 6.16
    R. De Mori: Computer Models of Speech Using Fuzzy Algorithms (Plenum, New York 1983)CrossRefGoogle Scholar
  17. 6.17
    N.J. Nilsson: Principles of Artificial Intelligence (Springer, Berlihn, Heidelberg 1982)MATHGoogle Scholar
  18. 6.18
    R. Bellman, S. Dreyfus: Applied Dynamic Programming (Princeton, Univ. Press, Princeton 1962)MATHGoogle Scholar
  19. 6.19
    Y. Ohta, T. Kanade, T. Sakai: An analysis system for scenes containing objects with substructures. Proc. 4th Int. Joint Conf. on Pattern Recognition, Kyoto (1978) pp.752–754Google Scholar
  20. 6.20
    M. Nagao, T. Matsuyama: A Structural Analysis of Complex Aerial Photographs (Plenum, New York 1980)CrossRefGoogle Scholar
  21. 6.21
    M. Yachida, S. Tsuji: A versatile machine vision system for complex industrial parts. IEEE Trans. C-26, 882–894 (1977)Google Scholar
  22. 6.22
    H. Nieman, H. Bunke, I. Hofmann, G. Sagerer, F. Wolf, H. Feistel: A knowledge based system for analysis of gated blood pool studies. IEEE Trans. PAMI-7 246–259 (1985)Google Scholar
  23. 6.23
    S. Rubin: The ARGOS image understanding system. Tech. Rpt. (Dept. Comput. Sci., Carnegie-Mellon Univ., Pittsburgh, PA (1978)Google Scholar
  24. 6.24
    W. Woods, M. Bates, G. Brown, B. Bruce, C. Cook, J. Klovstad, J. Makhoul, B. Nash-Webber, R. Schwartz, J. Wolf, V. Zue: Speech understanding systems, Vol.1, Introduction and Overview, Final Report, Bolt Beranek and Newman Inc., Cambridge, MA (1976)Google Scholar
  25. 6.25
    W.A. Woods: Optimal search strategies for speech understanding control. Artif. Intelligence 18, 295 (1982)CrossRefGoogle Scholar
  26. 6.26
    W.H. Paxton: Experiments in speech understanding system control. Tech. Note 134, Artif. Intell. Center, Stanford Res. Inst., Menlo Park, CA (1976)Google Scholar
  27. 6.27
    G. Sagerer, F. Kümmert: Knowledge based systems for speech understanding. In [Ref.3.178, pp.421–458]Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Heinrich Niemann
    • 1
    • 2
  1. 1.Lehrstuhl für Informatik 5 (Mustererkennung)Friedrich-Alexander-Universität Erlangen-NürnbergErlangenFed. Rep. of Germany
  2. 2.Forschungsgruppe WissensverarbeitungBayerisches Forschungszentrum für Wissensbasierte SystemeErlangenFed. Rep. of Germany

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