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TECHNIQUE OF FUZZY TUNED STOCHASTIC SCANPATHS FOR ROBOT VISION

Journal Name:

  • Istanbul University Journal of Electrical & Electronics Engineering

Publication Year:

  • 2004

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
The real-time processing of frame sequences obtained from cameras mounted on autonomous mobile robots and vehicles is a computationally intensive task. This paper is a review of the work carried out so far in the development of a procedure using fuzzy-tuned stochastic ‘scanpaths’ for efficiently scanning images in a frame sequence. A concise explanation of using fuzzy-tuned stochastic scanpaths is given here followed by a summary of the experimental work that has been undertaken to date and results achieved. The results show how the technique can reliably locate objects in scene whilst examining only a fraction of the image surface, e.g. 5%. The paper concludes with a discussion on research insofar accomplished and proposes ideas for future work.
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  • 1
1031-1043
  • English

REFERENCES

References: 

[1] M. J. Allen, Q. Mehdi, I. J. Griffiths, N. E. Gough, and I. M. Coulson, “Object location in colour images using fuzzy-tuned scanpaths and neural networks.” In Proceedings of the 19th SGES Int. Conf. on Knowledge Based Systems and Applied Artificial Intelligence, Cambridge, UK, pp. 302-314, 1999.
[2] M. J. Allen, Q. Mehdi, and N. E. Gough. “Stochastic scanpaths for efficient frame sequence analysis.” In Proceedings of the 12th SCS European Simulation Symposium, The Society for Computer Simulation International, pp. 185–189, 2000.
[3] M. J. Allen, I. J. Griffiths, I. M. Coulson, Q. Medhi, and N. E. Gough, “Efficient tracking of coloured objects using fuzzy-tuned scanpaths.” In Proceedings of the Workshop on Recent Advances in Soft-Computing, De Monfort University, Leicester, UK, pp. 225-230, 1999.
[4] M. J. Allen, Artificial Intelligence Techniques for Efficient Object Location in Image Sequences, PhD Thesis, University of Wolverhampton, Wolverhampton, UK, 2001.
[5] M. J. Allen, G. M. Dimirovski, N. E. Gough, and Q. H. Mehdi, “Fuzzy tuned stochastic
Technique Of Fuzzy Tuned Stochastic Scanpaths For Robot Vision 1041
Michael J. ALLEN, Georgi M. DIMIROVSKI, Norman E. GOUGH, Quas H. MEHDI
scanpaths for robot vision: A Development Review.” In J. J. J. Senteneiro, M. Athans, and A. Pascoal (eds.) Proceedings of the 10th IEEE Mediterranean Conf. on Control and Automation, Paper WA21/med-450/pp. 1-8. The IEEE and Instituto Superior Tecnico, Lisbon, PT, 2002.
[6] J. Y. Aloimonos, I. Weiss, and A. Bandyopadhyay. “Active vision ”, Int. J. of Computer Vision, vol. 2, pp. 333-356, 1987.
[7] P. R. Chapman, and G. Underwood. “Visual search of dynamic scenes: Event types and the role of experience in viewing driving situations”, In Eye Guidance in Reading and Scene Perception, Elsevier Science, Oxford, UK, pp. 369–394, 1998.
[8] G. M. Dimirovski, N. E. Gough, O. L. Iliev, A. T. Dinibutun, and O. Kaynak. “A contribution to fuzzy-logic analytical-simulation methods for AGV motion navigation.” In O. Kaynak, M. Ozkan, I. Tunay (Eds.) Proceedings ICRAM95 on Recent Advances in Mechatronics, Bogazici University, Istanbul, TR, vol. II, pp. 678-683, 1995.
[9] G. M. Dimirovski, Z. M. Gacovski, K. Schlacher, and O. Kaynak. “Mobile non-holonomic robots: On trajectory generation and obstacle avoidance.” In P. Kopacek (ed.) Robot Control (SYROCO 2001), Pergamon Elsevier Science, Oxford, UK, pp. 545-554, 2001.
[10] I. J. Griffiths, Q. H. Mehdi, and N. E. Gough. “Fuzzy tuned stochastic scanpaths for AGV vision”, In Proceedings of the Int. Conf. on Artificial Neural Networks and Genetic Algorithms, Norwich, UK, pp. 88-92, 1997.
[11] S. Haykin, Neural Netwroks: A Comprehen- sive Foundation (2nd edition). Prentice Hall, Upper Saddle River, NJ, 1999.
[12] J. M. Hendersen, and A. Hollingworth. “Eye movements during scene viewing: An overview,” In Eye Guidance in Reading and Scene Perception, Elsevier Science, Oxford, UK, pp. 269-294, 1998.
[13] J. M. Henderson, P. A. Weeks Jr., and A. Hollingworth, “Effects of semantic consistency on eye movements during scene viewing,” J. of Experimental Psychology: Human Perception and Performance, vol. 25, no. 1, pp. 210-228, 1999.
[14] D. Norton, and L. Stark. “Scanpaths in eye movements during pattern perception,” Science, vol. 171, pp. 308-311, 1971.
[15] G. C. Paull, and D. J. Glencross. “Expert perception and decision making in baseball”, Int. J. of Sport Psychology, vol. 28, pp. 35-56, 1997.
[16] L. W. Stark, C. M. Privitera, H. Yang, M. Azzariti, Y. F. Ho, T. Blackmon, and C. Dimitri, “Representation of human vision in the brain: How does human perception recognize images,” J. of Electronic Imaging, vol. 10, no. 1, pp. 123-151, 2001.
[17] J. Yen and R. Langari, Fuzzy Logic: Intelli-gence, Control, and Information. Prentice Hall, Upper Saddle River, NJ, 1999.

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