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Some Problems in Trying to Implement Uncertainty Techniques in Automated Inspection

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Applications of Uncertainty Formalisms

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1455))

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Abstract

This paper discusses the difficulties in applying uncertainty management techniques to real world problems. Automated Inspection is a process where the data used to model the environment is uncertain. There is an existing body of knowledge within the research community which enables such uncertain information to be expressed. Although there have been successful applications in fields such as medical diagnosis, there are also problems in industry which currently cannot be solved. The process of industrial inspection is an environment where the method for applying uncertainty management techniques is not intuitive. The nature of the uncertainty and the difficulty in applying the theoretical techniques to real world problems shall be the focus of the following discussion.

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References

  1. Bayro-Corrachano E, Review of Automated Visual Inspection 1983–1993, Part II: Approaches to Intelligent Systems, SPIE, Vol. 2055, 1993, 159–172

    Google Scholar 

  2. Bonissone P, Reasoning Plausible, in ‘Encyclopedia of Artificial Intelligence’, S Shapiro, 1992

    Google Scholar 

  3. Chou PB, RA Rao, MC Sturzenbecker, VH Brecker, Automatic defect classification for integrated circuits, SPIE Vol. 1907 Machine Vision applications in industrial inspection, pp 95–103, 1993

    Article  Google Scholar 

  4. D’Haeyer J, Reliable Flaw classifiers for machine vision based quality control, SPIE Vol. 2597, pp 119–130, 1995

    Google Scholar 

  5. Gel Count Forum-Raw Data, Image Automation Ltd., Texas, USA, June 1993

    Google Scholar 

  6. Holmes J, Technical Note-Setting up the L30, Image Automation Ltd., Sept. 1994

    Google Scholar 

  7. Krause P and Clark D, Representing Uncertain Knowledge an Artificial Intelligence Approach, Intellect Books, 1993

    Google Scholar 

  8. Lu N, Tredgold A and Fielding E, The use of machine vision and fuzzy sets to classify soft fruit, SPIE Vol. 2620, pp 663–669, 1995.

    Google Scholar 

  9. Luria M, Moran M, Yaffe D and Kawski J, Automatic defect classification using Fuzzy Logic, IEEE / SEMI Advanced Semiconductor Manufacturing Conference, p191–193, 1993

    Google Scholar 

  10. Perner P, A knowledge based image inspection system for automatic defect recognition, classification and process diagnosis, Machine Vision and Applications, 7:pp 135–147 1994

    Article  Google Scholar 

  11. Petrou M, Automated intelligent inspection for quality control, Invited presentation, Sira Technology Centre Intelligent Imaging Programme, General Meeting, 7 June 1995.

    Google Scholar 

  12. Raafat H and Taboun S, An integrated robotic and machine vision system for surface flaw detection and classification, Computers Industrial Engineering, Vol.30, No.1 pp27–40.

    Google Scholar 

  13. Rao R and Jain R, A classification scheme for visual defects arising in semiconductor wafer inspection, Journal of Crystal Growth, 103 pp398–406, 1990.

    Article  Google Scholar 

  14. Resin Grading and Gel Counting Technical User Forum, Image Automation Ltd., Texas, USA, June 1993

    Google Scholar 

  15. Saffiotti A, Issues of knowledge representation in Dempster-Shafer Theory, in Advances in the Dempster-Shafer Theory of Evidence, Ed. Yager RR, Kacprzyk J, Fedrizzi M, John Wiley, 1994.

    Google Scholar 

  16. Shafer G, A Mathematical Theory of Evidence, Princeton University Press, 1976

    Google Scholar 

  17. Sherman R, Tirosh E and Smilansky Z, An automatic defect classification system for semiconductor wafers, SPIE Vol. 1907 Machine Vision applications in industrial inspection, pp72–79, 1993

    Google Scholar 

  18. Shortliffe, E.H., Rule Based Expert Systems, the MYCIN Experiments of the Stanford Heuristics Programming Project, Addison Wesley 1985

    Google Scholar 

  19. Zadeh L, Knowledge Representation in Fuzzy Logic, pp 1–26, in An introduction to Fuzzy Logic applications in intelligent systems, ed R Yager, Kluwer 1992

    Google Scholar 

  20. Zimmerman HJ, Fuzzy Set Theory and its applications, Kluwer Academic Publishers, USA, 1991

    Book  Google Scholar 

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© 1998 Springer-Verlag Berlin Heidelberg

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Wilson, D., Greig, A., Gilby, J., Smith, R. (1998). Some Problems in Trying to Implement Uncertainty Techniques in Automated Inspection. In: Hunter, A., Parsons, S. (eds) Applications of Uncertainty Formalisms. Lecture Notes in Computer Science(), vol 1455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49426-X_11

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  • DOI: https://doi.org/10.1007/3-540-49426-X_11

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  • Print ISBN: 978-3-540-65312-7

  • Online ISBN: 978-3-540-49426-3

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