Evolving task specific algorithms for machine vision applications

MJ Callaghan; TM McGinnity; Liam McDaid

doi:10.1109/ICITA.2005.134

Evolving task specific algorithms for machine vision applications

MJ Callaghan, TM McGinnity, Liam McDaid

Research output: Chapter in Book/Report/Conference proceeding › Conference 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.

Original language	English
Title of host publication	Unknown Host Publication
Place of Publication	Sydney Australia
Publisher	IEEE
Pages	371-374
Number of pages	3
Volume	1
ISBN (Print)	0-7695-2316-1
DOIs	https://doi.org/10.1109/ICITA.2005.134
Publication status	Published - 7 Jul 2005
Event	Third International Conference on Information Technology and Applications, 2005. ICITA 2005. - Sydney Australia Duration: 7 Jul 2005 → …

Conference

Conference	Third International Conference on Information Technology and Applications, 2005. ICITA 2005.
Period	7/07/05 → …

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Keywords

Image processing
machine vision

Cite this

Callaghan, MJ., McGinnity, TM., & McDaid, L. (2005). Evolving task specific algorithms for machine vision applications. In Unknown Host Publication (Vol. 1, pp. 371-374). Sydney Australia: IEEE. https://doi.org/10.1109/ICITA.2005.134

@inproceedings{b976a39d53464052b8b82b189ead78b5,

title = "Evolving task specific algorithms for machine vision applications",

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.",

keywords = "Image processing, machine vision",

author = "MJ Callaghan and TM McGinnity and Liam McDaid",

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.",

year = "2005",

month = "7",

day = "7",

doi = "10.1109/ICITA.2005.134",

language = "English",

isbn = "0-7695-2316-1",

volume = "1",

pages = "371--374",

booktitle = "Unknown Host Publication",

publisher = "IEEE",

address = "United States",

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TY - GEN

T1 - Evolving task specific algorithms for machine vision applications

AU - Callaghan, MJ

AU - McGinnity, TM

AU - McDaid, Liam

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.

PY - 2005/7/7

Y1 - 2005/7/7

N2 - 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.

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

KW - machine vision

U2 - 10.1109/ICITA.2005.134

DO - 10.1109/ICITA.2005.134

M3 - Conference contribution

SN - 0-7695-2316-1

VL - 1

SP - 371

EP - 374

BT - Unknown Host Publication

PB - IEEE

CY - Sydney Australia

ER -

Access to Document

10.1109/ICITA.2005.134