Evolving task specific algorithms for machine vision applications

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Place of PublicationSydney Australia
Pages371-374
Number of pages3
Volume1
DOIs
Publication statusPublished - 7 Jul 2005
EventThird International Conference on Information Technology and Applications, 2005. ICITA 2005. - Sydney Australia
Duration: 7 Jul 2005 → …

Conference

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

Fingerprint

Computer vision
Evolutionary algorithms
Case based reasoning
Knowledge acquisition
Knowledge based systems
Intelligent systems
Engineers

Keywords

  • Image processing
  • machine vision

Cite this

Callaghan, MJ ; McGinnity, TM ; McDaid, Liam. / Evolving task specific algorithms for machine vision applications. Unknown Host Publication. Vol. 1 Sydney Australia, 2005. pp. 371-374
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abstract = "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.",
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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, Tokyo [7] U.Rost, H. M{\"u}nkel, (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, Netherlands [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, 1994. [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.",
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Callaghan, MJ, McGinnity, TM & McDaid, L 2005, Evolving task specific algorithms for machine vision applications. in Unknown Host Publication. vol. 1, Sydney Australia, pp. 371-374, Third International Conference on Information Technology and Applications, 2005. ICITA 2005., 7/07/05. https://doi.org/10.1109/ICITA.2005.134

Evolving task specific algorithms for machine vision applications. / Callaghan, MJ; McGinnity, TM; McDaid, Liam.

Unknown Host Publication. Vol. 1 Sydney Australia, 2005. p. 371-374.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N1 - 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, Tokyo [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, Netherlands [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, 1994. [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.

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AB - 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.

KW - Image processing

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BT - Unknown Host Publication

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