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From multidimensional signals to the generation of responses

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Algebraic Frames for the Perception-Action Cycle (AFPAC 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1315))

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Abstract

It has become increasingly apparent that perception cannot be treated in isolation from the response generation, firstly because a very high degree of integration is required between different levels of percepts and corresponding response primitives. Secondly, it turns out that the response to be produced at a given instance is as much dependent upon the state of the system, as the percepts impinging upon the system. The state of the system is in consequence the combination of the responses produced and the percepts associated with these responses. Thirdly, it has become apparent that many classical aspects of perception, such as geometry, probably do not belong to the percept domain of a Vision system, but to the response domain.

There are not yet solutions available to all of these problems. In consequence, this overview will focus on what are considered crucial problems for the future, rather than on the solutions available today. It will discuss hierarchical architectures for combination of percept and response primitives, and the concept of combined percept-response invariances as important structural elements for Vision. It will be maintained that learning is essential to obtain the necessary flexibility and adaptivity. In consequence, it will be argued that invariances for the purpose of vision are not geometrical but derived from the percept-response interaction with the environment. The issue of information representation becomes extremely important in distributed structures of the types foreseen, where uncertainty of information has to be stated for update of models and associated data.

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References

  1. D. Beymer and T. Poggio. Image Representations for Visual Learning. Science, 272:1905–1909, June 1996.

    PubMed  Google Scholar 

  2. D. Gabor. Theory of communication. J. Inst. Elec. Eng., 93(26):429–457, 1946.

    Google Scholar 

  3. J.-H. Gao, L. M. Parsons, J. M. Bower, J. Xiong, J. Li, and P. T. Fox. Cerebellum Implicated in Sensory Acquisition and Discrimination Rather Than Motor Control. Science, 272:545–547, April 1996.

    PubMed  MathSciNet  Google Scholar 

  4. G. H. Granlund. In search of a general picture processing operator. Computer Graphics and Image Processing, 8(2):155–178, 1978.

    Google Scholar 

  5. G. H. Granlund. Integrated analysis-response structures for robotics systems. Report LiTH-ISY-I-0932, Computer Vision Laboratory, Linköping University, Sweden, 1988.

    Google Scholar 

  6. G. H. Granlund and H. Knutsson. Signal Processing for Computer Vision. Kluwer Academic Publishers, 1995. ISBN 0-7923-9530-1.

    Google Scholar 

  7. W. E. L. Grimson. Object Recognition by Computer: The Role of Geometric Constraints. MIT Press, Cambridge, MA. USA, 1990.

    Google Scholar 

  8. L. Haglund, H. Knutsson, and G. H. Granlund. Scale and Orientation Adaptive Filtering. In Proceedings of the 8th Scandinavian Conference on Image Analysis, Tromsö, Norway, May 1993. NOBIM. Report LiTH-ISY-I-1527, Linköping University.

    Google Scholar 

  9. R. Held and A. Hein. Movement-produced stimulation in the development of visually guided behavior. Journal of Comparative and Physiological Psychology, 56(5):872–876, October 1963.

    PubMed  Google Scholar 

  10. R. I. G. Hughes. The structure and interpretation of quantum mechanics. Harvard University Press, 1989. ISBN: 0-674-84391-6.

    Google Scholar 

  11. L. Jacobsson and H. Wechsler. A paradigm for invariant object recognition of brightness, optical flow and binocular disparity images. Pattern Recognition Letters, 1:61–68, October 1982.

    Article  Google Scholar 

  12. K. Kanatani. Camera rotation invariance of image characteristics. Computer Vision, Graphics and Image Processing, 39(3):328–354, Sept. 1987.

    Google Scholar 

  13. L. C. Katz and C. J. Shatz. Synaptic activity and the construction of cortical circuits. Science, 274:1133–1138, November 15 1996.

    Article  PubMed  Google Scholar 

  14. J. J. Koenderink and A. J. van Doorn. Invariant properties of the motion parallax field due to the movement of rigid bodies relative to an observer. Opt. Acta 22, pages 773–791, 1975.

    Google Scholar 

  15. J. J. Koenderink and A. J. van Doorn. The structure of images. Biological Cybernetics, 50:363–370, 1984.

    Article  PubMed  MathSciNet  Google Scholar 

  16. T. Landelius. Behavior Representation by Growing a Learning Tree, September 1993. Thesis No. 397, ISBN 91-7871-166-5.

    Google Scholar 

  17. T. Landelius and H. Knutsson. A Dynamic Tree Structure for Incremental Reinforcement Learning of Good Behavior. Report LiTH-ISY-R-1628, Computer Vision Laboratory, S-581 83 Linköping, Sweden, 1994.

    Google Scholar 

  18. T. Landelius and H. Knutsson. Behaviorism and Reinforcement Learning. In Proceedings, 2nd Swedish Conference on Connectionism, pages 259–270, Skövde, March 1995.

    Google Scholar 

  19. T. Landelius and H. Knutsson. Reinforcement Learning Adaptive Control and Explicit Criterion Maximization. Report LiTH-ISY-R-1829, Computer Vision Laboratory, S-581 83 Linköping, Sweden, April 1996.

    Google Scholar 

  20. R. A. Lewitt. Physiological Psychology. Holt, Rinehart and Winston, 1981.

    Google Scholar 

  21. L. M. Lifshitz. Image segmentation via multiresolution extrema following. Tech. Report 87-012, University of North Carolina, 1987.

    Google Scholar 

  22. J. L. Mundy and A. Zisserman, editors. Geometric Invariance in Computer Vision. The MIT Press, Cambridge, MA. USA, 1992. ISBN 0-262-13285-0.

    Google Scholar 

  23. K. Nordberg, G. Granlund, and H. Knutsson. Representation and Learning of Invariance. In Proceedings of IEEE International Conference on Image Processing, Austin, Texas, November 1994. IEEE.

    Google Scholar 

  24. T. Poggio and S. Edelman. A network that learns to recognize three-dimensional objects. Nature, 343:263–266, 1990.

    Article  PubMed  Google Scholar 

  25. J. L. Raymond, S. G. Lisberger, and M. D. Mauk. The Cerebellum: A Neuronal Learning Machine? Science, 272:1126–1131, May 1996.

    PubMed  Google Scholar 

  26. G. M. Shepherd. The Synaptic Organization of the Brain. Oxford University Press, 2nd edition, 1979.

    Google Scholar 

  27. S. Ullman and R. Basri. Recognition by linear combinations of models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(10):992–1006, 1991.

    Article  Google Scholar 

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Gerald Sommer Jan J. Koenderink

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© 1997 Springer-Verlag Berlin Heidelberg

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Granlund, G.H. (1997). From multidimensional signals to the generation of responses. In: Sommer, G., Koenderink, J.J. (eds) Algebraic Frames for the Perception-Action Cycle. AFPAC 1997. Lecture Notes in Computer Science, vol 1315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017859

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  • DOI: https://doi.org/10.1007/BFb0017859

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63517-8

  • Online ISBN: 978-3-540-69589-9

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