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Artificial Neural Nets and Genetic Algorithms pp 136–139Cite as

Artificial Neural Networks for Motion Estimation

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Abstract

This paper deals with the using of Artificial Neural Networks (ANN) for motion estimation. By means of simple neural structures it is possible to improve the reliability and accuracy of block matching algorithms (BMA) by a postprocessing of the similarity criterion. The ANN dimensions the appropriate structures. The fundamental idea and some first results will be described. The performance capability of the proposed method is shown for selected synthetic one- and real world two-dimensional measuring situations which are not solvable by means of conventional BMA.

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References

  1. Convertino, G.; et. al.: Hopfield: Neural Network for Motion Estimation and Interpretation. Proc. ICANN’ 94, Sorrento, Italy, 26–29 May 1994, Vol. 1, pp. 78–81.

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  2. Schnelting, O.; Seiffert, U.; Michaelis,B.: Bewegungsschätzung mit künstlichen neuronalen Netzen. 4. Dortunder Fuzzy-Tage’94 Dortmund, Germany.

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  3. Zaagman, W.H.; et. al.: On the Correlation Model: Performance of a Movement Detecting Neural Element in the Fly Visual System. Biological Cybernetics 31 (1978), pp. 163–178.

    Article  Google Scholar 

  4. Poggio, T.; Reichart, W.: Considerations on Models of Movement Detection. Kybernetik 13 (1973), pp. 223–227.

    Article  Google Scholar 

  5. Seiffert, U; Michaelis, B.: Estimating Motion Parameters from Image Sequences by Self-Organizing Maps. Proc. 39. IWK Ilmenau, 1994.

    Google Scholar 

  6. Musmann, H.-G.; Pirsch, P.; Grallert, H.-J.: Advances in Picture Coding. Proc. IEEE 73 (1985) No. 4, pp. 523–530.

    Article  Google Scholar 

  7. Wiener, N.: The Extrapolation, Interpolation and Smoothing of Stationary Time Series. Wiley & Sons, New York, 1949.

    Google Scholar 

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Authors and Affiliations

  1. Institute of Process Measurement Technology and Electronics, Otto-von-Guericke University of Magdeburg, 39106, Magdeburg, Universitätsplatz 2, Germany

    Olaf Schnelting, Bernd Michaelis & Rüdiger Mecke

Authors
  1. Olaf Schnelting
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  2. Bernd Michaelis
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  3. Rüdiger Mecke
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© 1995 Springer-Verlag/Wien

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Cite this paper

Schnelting, O., Michaelis, B., Mecke, R. (1995). Artificial Neural Networks for Motion Estimation. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_37

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  • DOI: https://doi.org/10.1007/978-3-7091-7535-4_37

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-82692-8

  • Online ISBN: 978-3-7091-7535-4

  • eBook Packages: Springer Book Archive

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