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Efficient Image Sequence Analysis Using Fuzzy Techniques

  • M. J. Allen
  • Q. H. Mehdi
  • N. E. Gough
  • I. M. Coulson
Part of the Advances in Soft Computing book series (AINSC, volume 9)

Abstract

Machine Vision Systems (MVSs) used, for example in mobile robots, have to function in real-time and there is a need to develop more efficient ways of processing frame sequences. The problem can be addressed by mimicking aspects of human vision [1–4]. Previous work by Griffiths et al described a process for examining scenes using a camera mounted on a pan and tilt unit. This process used stochastic scanpaths that were tuned using a Fuzzy Inference System (FIS) to determine the next orientation of the camera. The development of this technique for scanning image frames, captured offline by a digital video camera, is described in Allen et al [6]. This paper introduces the concept of Fuzzy tuned scanpaths in Section 2. Section 3 examines the implementation and operation of an adaptive technique. Experimental work using this adaptive technique is described in Section 4 and conclusions are given in Section 5.

Keywords

Fuzzy Inference System System Configuration Digital Video Camera Frame Sequence Adaptive Technique 
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.

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References

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    Hubel, D. H., Eye, brain and vision, Scientific American Library, p36 (1995).Google Scholar
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    Hacisalihzade, S., L. Stark, J. Allen, Visual Perception and sequences of eye movements: A stocahastic modelling approach, IEEE Trans. On Systems, Man and Cybernetics, 22 (3), pp 474–481 (1992).CrossRefGoogle Scholar
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    Griffiths I. J., Q. H. Mehdi, N. E. Gough, Fuzzy tuned stochastic scanpaths for AGV vision, Int. Conf. on Artificial Neural Networks and Genetic Algorithms–ICANNGA ‘87, Norwich, UK, pp 88–92 (1997).Google Scholar
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    Allen M. J., Mehdi Q., Griffiths I. J., Gough N., Coulson I. M., Object location in colour images using fuzzy-tuned scanpaths and neural networks, Proc. 19’“ SGES Int. Conf. Knowledge Based Systems and Applied Artificial Intelligence, Cambridge, UK, pp 302–314 (1999).Google Scholar
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    Jain R., R. Kasturi, B. G. Schunck, Machine Vision, McGraw Hill, (1995).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • M. J. Allen
    • 1
  • Q. H. Mehdi
    • 1
  • N. E. Gough
    • 1
  • I. M. Coulson
    • 1
  1. 1.University of WolverhamptonUK

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