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Applied active databases for evolving image processing algorithms

  • W. Naqvi
  • S. Panyiotou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 978)

Abstract

To develop an algorithm for any application takes thought and a lot of trial and error. The algorithm must be coded, compiled, tested for compliance with the specification. If it does not perform to target, the code must be amended, recompiled and tested again. The process is cyclic and time consuming. In this paper a novel method is introduced which allows the building or tuning of algorithms or programs at run-time by using an active database. The paper uses the domain of robotic vision as a case study to introduce the concept, particularly the first stage of the object recognition process known assegmentationi.e. extracting the primitive characteristics of the objects of interest. The system has been implemented upon the REFLEX active database system.

Keywords

active database mutating algorithms active algorithms segmentation algorithms image processing knowledgebase systems active application REFLEX 

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References

  1. [1]
    Agrawal R. and Gehani N.H., “Rationale for the Design of Persistence and Query Processing Facilities in the Database Programming Language O++”, 2nd Int. Workshop on Database Programming Languages, Portland, OR, June 1989Google Scholar
  2. [2]
    Aleksander I., Thomas W.V. and Bowden P.A., “WISARD — A Radical Step Forward in Image Recognition”, Sensor Review. July 1984Google Scholar
  3. [3]
    Chakravarthy S., Blaustein B., Buchmann A., et al., “HiPAC: A Research Project in Active, Time-Constrained Database Management”, Final Technical Report, Xerox Advanced Information Technology Division, July 1989Google Scholar
  4. [4]
    De Haas L.J., “Automatic Programming of Machine Vision Systems”, Proceedings of the International Joint Conference on Artificial Intelligence, 1987Google Scholar
  5. [5]
    Diaz O., Paton N.W. and Gray P., “Rule Management in Object-Oriented Databases: A Uniform Approach”, Proc. of the 17th Int. Conf. on Very Large data Bases, Barcelona, Spain, 1991Google Scholar
  6. [6]
    Genesereth M.R. and Nilsson N.J., “Logical Foundation of Artificial Intelligence”, Los Altos, California: Morgan Kaufmann, 1987Google Scholar
  7. [7]
    Iwase H., Toriu T. and Gotoh T., “An Expert System for image processing”, Proceedings of the Fourth Conference on Artificial Intelligence Applications, San Diego, March 1988Google Scholar
  8. [8]
    Marr D., “Vision”, Freeman, San Francisco, 1982Google Scholar
  9. [9]
    McCarthy D.R. and Dayal U., “The Architecture of an Active Data Base Management System”, Proc. ACM SIGMOD Intl. Conf. on Management of Data, Portland, June 1989Google Scholar
  10. [10]
    Naqvi W. and Ibrahim M.T., “REFLEX Active Database Model: Application of Petri-Nets”, Proc. of the 4th Int. Conf. on Database and Expert Systems Applications, Prague, September 1993Google Scholar
  11. [11]
    Naqvi W. and Ibrahim M.T., “Rule and Knowledge Management in an Active Database System”, Proc. of 1st Int. Workshop. on Rules in Database Systems, Edinburgh, September 1993Google Scholar
  12. [12]
    Naqvi W. and Ibrahim M.T., “EECA: An Active Knowledge Model”, Proc. of the 5th Int. Conf. on Database and Expert Systems Applications, Athens, September 1994Google Scholar
  13. [13]
    Naqvi W. and Ibrahim M.T., “Active Distribution by Stealth”, Proc. of the 6th Int. Conf. on Database and Expert Systems Applications (workshop), London, September, 1995Google Scholar
  14. [14]
    Naqvi W., Panayiotou S., Soper A. and Ibrahim M.T, “Cortextual Parsing: The use of an active database to provide semi-evolving segmentation algorithms”, Tech. Report CIT-DSRL069301, University of Greenwich, June, 1993Google Scholar
  15. [15]
    “ONTOS Reference Manual”, ONTOS Inc, 1991Google Scholar
  16. [16]
    “POET 2.1 Programmer's & Reference Guide”, POET Software Corporation, 1994Google Scholar
  17. [17]
    Sadjadi F. and Nasr H., “A technique for automatic design of image segmentation algorithms”, Proceedings of the SPIE-The Int. Society for Optical Engineering, Vol: 1098 p. 177–81, 1989Google Scholar
  18. [18]
    Stonebraker M. and Kemnitz G., “The POSTGRES Next-Generation Database Management System”, CACM October 1991, Vol 34, No 10Google Scholar
  19. [19]
    Subbarao M., “Interpretation of Visual Motion: A Computational Study”, Morgan Kaufmann Publishers, 1988Google Scholar
  20. [20]
    Wilensky, “Planning and Understanding”, Reading, Addison Wesley, 1983Google Scholar
  21. [21]
    Lohman G. M., Lindsay B., Pirahesh H. and Schiefer K. B., “Extensions To STARBURST: Objects, Types, Functions, and Rules”, CACM October 1991, Vol 34, No 10Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • W. Naqvi
    • 1
  • S. Panyiotou
    • 1
  1. 1.School of Computing and Mathematical SciencesUniversity of GreenwichLondonUK

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