Advertisement

Adaptive Image Segmentation Based on Visual Interactive Feedback Learning

  • Praminda Caleb-Solly
  • Jim Smith

Abstract

The advent of cheap, reliable high speed processing has lead to a greater integration of adaptive computing technology into different stages of the manufacturing process, such as the real-time detection and identification of defects on the production line. In this paper we describe the use of an interactive evolutionary algorithm to capture human knowledge in the form of a set of parameters. These parameters control the processing of a set of images from hot-rolled steel surfaces in order to extract “regions of interest”; the resulting segmented images are fed into a defect detection and classification system. In order for this classification system to work correctly it is necessary to extract “regions of interest” with a high degree of accuracy. In a noisy environment, with changing user requirements, there exists a need to easily adapt the parameters to reflect the changing circumstances. We show, that providing the algorithm is suitably tailored to avoid problems of user fatigue, it is possible to evolve optimum parameter sets based on the user’s visual evaluation and grading of the resulting segmentations, with corresponding benefits for the manufacturing process.

Keywords

Texture Image Segmented Image Texture Measure Interactive Evolutionary Computation Target Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Caleb, P., Steuer, M.; (2000) Classification of Surface Defects on Hot Rolled Steel Using Adaptive Learning Methods. Proceedings of the KES’2000 Fourth International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies, Brighton, UK, 2000, Vol. I, 103–108, 0-7803-6400-7 IEEE.Google Scholar
  2. 2.
    Herdy, M. (1996) Evolution Strategies with Subjective Selection. Lecture Notes in Computer Science 1142, Intl. Conf. On Evolutionary Comp, Parallel Problem Solving from Nature, pp 22–31.Google Scholar
  3. 3.
    Dawkings, R., (1987) The Blind Watchmaker. WW Norton and Company.Google Scholar
  4. 4.
    Baker, E., Seltzer, M., (1994): “Evolving Line Drawings” Graphics Interface 94 Procs, Morgan Kaufmann, <http://citeseer.nj.nec.com/aker94evolving.html Google Scholar
  5. 5.
    Biles, J. A. (1994). GenJam: A genetic algorithm for generating jazz solos. In ICMC Proceedings 1994. The Computer Music Association.Google Scholar
  6. 6.
    Takagi, H., Ohsaki M. (1999): “IEC-based Hearing Aid Fitting”. Proceedings of Int’l Conf. On System, Man and Cybernetics (SMC′99), Vol 3, 657–662 IEEE.Google Scholar
  7. 7.
    Takagi, H. (1998) “Interactive Evolutionary Computation: System Optimization Based on Human Subjective Evaluation”. The IEEE International Conference on Intelligent Engineering Systems (INES′98) Vienna, Austria, September 17-19, 1998, (INES 98)Google Scholar
  8. 8.
    Haralick, R.M., Shanmugam, K., Dinstein, I. (1973) Texture Feature for Image Classification. IEEE Transactions on Systems, Man and Cybernetics, Vol. SMC-3, No 6, November, pp 610–620.CrossRefGoogle Scholar
  9. 9.
    Thomas Back, Frank Hoffmeister, and Hans-Paul Schwefel.(1991) A survey of evolution strategies. In Lashon B. Belew, Richard K. Booker, editor, Proceedings of the 4th International Conference on Genetic Algorithms, pages 2–9, San Diego, CA, July 1991. Morgan Kaufmann..Google Scholar
  10. 10.
    Schwefel HP (1997). Evolutionary computation — A study on collective learning. Proc. World Multiconference on Systemics, Cybernetics and Informatics (SC′97), vol 2, pp 198–205Google Scholar

Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  1. 1.Faculty of Computing, Engineering and Mathematical SciencesUniversity of the West of EnglandBristolUK

Personalised recommendations