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About some perception problems in neural networks

  • Neural Networks for Perception
  • Conference paper
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From Natural to Artificial Neural Computation (IWANN 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 930))

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Abstract

Perception is a major problem which is studied as well in life science as in engineering science: This paper concerns a reflection about some of the early mechanisms which underlie perception. Examples are taken in the field of vision in biology and in computer vision, showing the necessity of some adequate pre-processing of the signals. Then, perception appears as a process of representation of signals, that is, as a process of data analysis aimed at finding the structure of the data. Two examples of artificial neural networks are presented to illustrate the problem of data representation. The first one called “Independent Component Analysis” is close to the signal level, the second one, called “Curvilinear Component Analysis” can be seen as a smooth transition between the aspects of Signal Processing and those of Data Analysis.

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References

  1. Adiv G., (1985) Determining 3-D Motion and Structure from Optical flow generated by Several Moving Objects. IEEE Trans. PAMI, vol. 7, N∘ 4.

    Google Scholar 

  2. Atick J., (1992) What does the Retina Know about Natural Scenes. Neural Computation, 4, 196–210.

    Google Scholar 

  3. Beaudot W, Palagi P, Hérault J., (1993) Realistic Simulation Tool for Early Visual Processing including Space, Time and Colour Data. International Workshop on Artificial Neural Networks IWANN'93, Barcelona, Spain

    Google Scholar 

  4. Cappellini V., Mecocci A., (1993) Motion Analysis and Representation in Computer Vision. Journal of Circuits, Systems and Computers, Vol. 3, N∘ 4.

    Google Scholar 

  5. Demartines P. (1994) Analyse de données par réseaux de neurones auto-organisés. Thèse de Doctorat de l'Institut National Polytechnique de Grenoble, 26 Nov. 1994.

    Google Scholar 

  6. Demartines P., Hérault J., (1993) Representation of non-linear data structures through fast VQP neural networks. Proceedings of Neuro-Nîmes'93, Nîmes (France).

    Google Scholar 

  7. Droulez J., Darlot C., (1989) The geometric and dynamic implications of the coherent constraints in three-dimensional sensorimotor interactions. Attention and Performance, 14, 4105–526.

    Google Scholar 

  8. Franceschini N., Pichon J. M., Blanes C. (1992) From insect vision to robot vision. Phil. Trans. Roy. Soc. London. B 337, 283–294.

    Google Scholar 

  9. Gersho A., Gray R. M. (1992) Vector quantization and signal compression. Kluwer Academic Publishers, London.

    Google Scholar 

  10. Hérault J., Jutten C. (1994) Réseaux neuronaux et traitement du signal. Traité des nouvelles technologies, Hermes, Paris.

    Google Scholar 

  11. Jutten C., Hérault J., (1991) Blind separation of sources. Part I: an adaptive algorithm based on a neuromimetic architecture. Signal Processing, Vol. 24, 1–10.

    Google Scholar 

  12. Kohonen T., (1984) Self-organisation of topologically correct feature maps. Biological cybernetics, 43, 59–69.

    Google Scholar 

  13. Liu X., Hérault J., (1991) Colour image processing by a neural network model. International Neural Network Conference INNC'91, July 9–13, Paris (France).

    Google Scholar 

  14. Livingstone M., Hubel D. H., (1988) Segregation of form, colour, movement and depth: anatomy, physiology and perception. Science, 240, 740–749.

    PubMed  Google Scholar 

  15. Marr D., (1982) Vision: a computational investigation into the human representation and processing of visual information. WH Freeman & Co, San Francisco

    Google Scholar 

  16. Nguyen Thi H. L., Jutten C., (1995) Blind source separation for convolutive mixtures. Signal Processing, (to appear).

    Google Scholar 

  17. Pettet M. W., Gilbert C. D., (1992) Dynamic changes in receptive-field size in cat primary visual cortex. Proceedings of National Academy of Science, USA, Vol. 89; 8366–8370.

    Google Scholar 

  18. Sammon W. J., (1969) A non-linear mapping algorithm for data structure analysis. IEEE Trans. on Computers, Vol. C-18, N∘ 5, 401–409.

    Google Scholar 

  19. Schölkopf B., Mallot H. P., (1994) View-based cognitive mapping and path planning. Technical report N∘ 7, Max Plank Institut für biologische Kybernetik, Tübingen, Germany.

    Google Scholar 

  20. Shepard R. N., Carrol J. D., (1965) Parametric representation of non-linear data structures. In International Symposium on Multivariate Analysis, Krishnaiah P. R, editor, Academic Press.

    Google Scholar 

  21. Sull S., Ahuja N., (1994) Integrated 3-D Analysis-Guided Synthesis of Flight Image Sequences. IEEE Trans. PAMI, Vol. 16, N∘ 4.

    Google Scholar 

  22. Sune J. L., Puget P., Samy R., (1993) Computation of the depth-from-motion problem from neural networks. Proceedings of Neuro-Nîmes'93, NÎMES (France).

    Google Scholar 

  23. Tononi G., Sporns O., Edelman G. M., (1992) Reentry and the problem of integrating multiple cortical areas: Simulation of dynamic integration in the visual system. Cerebral Cortex, Vol. 2, N∘ 4, 316–335.

    Google Scholar 

  24. Treisman A., (1988) Features and objects: the fourteenth Barlett Memorial Lecture. Quarterly Journal of Experimental Psychology, Vol. 40 A, 201–237.

    Google Scholar 

  25. Walter J. A, Schulten K. J., (1993) Implementation of Self-Organising Neural Networks for Visuo-Motor Control of an Industrial Robot. IEEE Trans. on Neural Networks, Vol. 4, N∘ 1.

    Google Scholar 

  26. Zupan L., (1995) Modélisation du réflexe vestibulo-occulaire et prédiction des cinétoses. Thèse de Doctorat, École Nationale Supérieure des Télécommunications, Paris (France).

    Google Scholar 

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Authors and Affiliations

  1. INPG - TIRF, 46 Avenue Félix Viallet, F-38031, Grenoble Cedex, France

    Jeanny Hérault

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  1. Jeanny Hérault
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José Mira Francisco Sandoval

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© 1995 Springer-Verlag Berlin Heidelberg

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Hérault, J. (1995). About some perception problems in neural networks. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_259

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  • DOI: https://doi.org/10.1007/3-540-59497-3_259

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-59497-0

  • Online ISBN: 978-3-540-49288-7

  • eBook Packages: Springer Book Archive

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