Evolving task specific algorithms for machine vision applications

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1 Citation (Scopus)


Increased use of machine vision system's are making a significant contribution to ensuring competitiveness in modern manufacturing. The development of task specific machine vision algorithms is a difficult process as there is no definitive model of the area so no generic approach to problem solving exists. Traditional approaches focused on the use of rule based systems to automate the generation of algorithms. However this type of approach suffers from issues related to the knowledge acquisition bottleneck and modeling of expertise. One possible solution to this problem is to evolve task specific algorithms using evolutionary tools. This work focuses on the use of an intelligent design tool that aids an engineer in designing machine vision algorithms using a hybrid intelligent system approach based around an evolutionary algorithm (EA), case based reasoning (CBR) and rule based reasoning (RBR) architectures.
Original languageEnglish
Title of host publicationUnknown Host Publication
Place of PublicationSydney Australia
Number of pages3
ISBN (Print)0-7695-2316-1
Publication statusPublished (in print/issue) - 7 Jul 2005
EventThird International Conference on Information Technology and Applications, 2005. ICITA 2005. - Sydney Australia
Duration: 7 Jul 2005 → …


ConferenceThird International Conference on Information Technology and Applications, 2005. ICITA 2005.
Period7/07/05 → …

Bibliographical note

Reference text: [1] Batchelor. B, (2003); "Machine vision for the
inspection of natural products" Springer-Verlag. New
York, NY, USA pp 35 - 86, ISBN:1-85233-525-4
[2] F.Grimm, H.Bunke (1993). "An expert system for
the selection and application of image processing
subroutines ". Expert Systems, vol.10, no.2, May
1993, pp.61-74. UK.
[3] J.Holt, J. Stocks, A.Thomas, M.G. Rodd, C.P.
Jobling, F.Deravi (1997). " Overview of an industrial
inspection workbench" : Proc. the 13th World
Congress, IFAC. Computer Control. 97; pp 363-8.
[4] R.Clouard, A.Elmoataz, C.Porquet, M. Revenu
(1999). "Borg: a knowledge-based system for
automatic generation of image processing". IEEETrans.-
on-Pat.-Analysis. vol.21, no.2; 1999; p.128-44.
[5] V.Clement, M.Thonnat (1993). "A knowledgebased
approach to integration of image processing
procedures. " CVGIP-Image Understanding, vol.57,
no.2, March 1993, pp.166-84. USA.
[6] O.Dehning, (1996). "Gipsy: Knowledge Based
Surface Inspection". MVA "96, IAPR Workshop on
Machine Vision Applications 12.-14. November 1996,
[7] U.Rost, H. Münkel, (1998) "Knowledge Based
Configuration of Image Processing Algorithms", Inter.
Conf. on Computational Intelligence (ICCIMA98),
[8] M.J. Callaghan, T.M. McGinnity, L McDaid, “
Third Order Loose Coupled Hybrid Intelligent System
for Machine Vision Applications,” IEEE SMC 2004
International Conference on Systems, Man and
Cybernetics. October 10-13 2004 The Hague,
[9] A.Chipperfield, P.Fleming, H Pohlheim, (1994).
"GA Toolbox for MATLAB". Proc. Int. Conf. Sys.
Engineering, Coventry, UK, 6-8 Sept., pp. 200-207,
[10] C. J. Price, I. S. Pegler, F. Bell, "Case-based
reasoning in the melting pot", International Journal of
Applied Expert Systems, volume 1(2), 1993.
[11] J. Giarratano, "Expert Systems: Principles and
Programming", Brooks Cole; 3rd Bk&Cdr edition
(February 9, 1998) ISBN: 0534950531
[12] H. Bassmann, P.Besslich, (1995). "Ad Oculos,
Digital Image Processing ", Thompson International
Press 1995.


  • Image processing
  • machine vision


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