Shape Recognition of Film Sequence with Application of Sobel Filter and Backpropagation Neural Network

  • A. Głowacz
  • W. Głowacz
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 60)

Abstract

A new approach to shape recognition is presented. This approach is based on Sobel filter and backpropagation neural network. Investigations of the shape recognition were carried out for film sequences. The aim of this paper is analysis of a system which enables shape recognition.

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References

  1. 1.
    Greene, E.: Simultaneity in the millisecond range as a requirement for effective shape recognition. Behav. Brain Funct. (2006), doi:10.1186/1744-9081-2-38Google Scholar
  2. 2.
    Golden, R.M.: Mathematical methods for neural network analysis and design. MIT Press, Cambridge (1996)Google Scholar
  3. 3.
    Anderson, J.A.: An introduction to neural networks. MIT Press, Cambridge (1995)MATHGoogle Scholar
  4. 4.
    Fausett, L.V.: Fundamentals of neural networks: architectures, algorithms, and application. Prentice-Hall, Englewood Cliffs (1994)MATHGoogle Scholar
  5. 5.
    Huang, K., Aviyente, S.: Wavelet feature selection for image classification. IEEE Transactions on Image Processing 17(9), 1709–1720 (2008)CrossRefGoogle Scholar
  6. 6.
    Papari, G., Petkov, N.: Adaptive pseudo dilation for gestalt edge grouping and contour detection. IEEE Transactions on Image Processing 17(10), 1950–1962 (2008)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Burger, T., Urankar, A., Aran, O., Akarun, L., Caplier, A.: Cued speech hand shape recognition (2007), http://www.lis.inpg.fr/pages_perso/burger/Publications/burger_aran_visapp.pdf (accessed April 25, 2009)
  8. 8.
    Cao, F., Delon, J., Desolneux, A., Musé, P., Sur, F.: A unified framework for detecting groups and application to shape recognition. J. Math. Imaging and Vision 27(2), 91–119 (2007)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Kotsia, I., Pitas, I.: Facial expression recognition in image sequences using geometric deformation features and support vector machines. IEEE Transactions on Image Processing 16(1), 172–187 (2007)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Tsai, L.W., Hsieh, J.W., Fan, K.C.: Vehicle detection using normalized color and edge map. IEEE Transactions on Image Processing 16(3), 850–864 (2007)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Grzymala-Busse, J.W., Hippe, Z.S., Roj, E., Skowroński, B.: Applying expert systems to hop extraction monitoring and prediction. Polish Journal of Chemical Technology 8(4), 1–3 (2006)Google Scholar
  12. 12.
    Przytulska, M., Kulikowski, J.L., Wierzbicka, D.: Analysis of irregular shape’s time-variations by serial contours enhancement. In: VIIP 2002 Conference on Visualization, Imaging, and Image Processing, Marbella, Spain (2002), http://www.actapress.com/Content_of_Proceeding.aspx?proceedingID=367# (accessed April 24, 2009)
  13. 13.
    Malik, J., Belongie, S., Leung, T.K., Shi, J.: Contour and texture analysis for image segmentation. Intern. J. Computer Vision 43(1), 7–27 (2001)MATHCrossRefGoogle Scholar
  14. 14.
    Belongie, S., Malik, J., Puzicha, J.: Shape context: a new descriptor for shape matching and object recognition. Adv. Neural Proc. Systems 13, 831–837 (2000)Google Scholar
  15. 15.
    Johnson, A., Bron, L., Brussee, P., Goede, I., Hoogland, S.: Learning from mistakes. In: Proc. Conf. on Human System Interaction, Maastricht, Netherlands, pp. 235–239 (2000)Google Scholar
  16. 16.
    Skaf, A., David, B., Descotes-Genon, B., Binder, Z.: General approach to man-machine system design: ergonomic and technical specification of actions. In: Proc. Conference on Human System Interaction, Maastricht, Netherlands, pp. 355–367 (2000)Google Scholar
  17. 17.
    Gavrila, D., Philomin, V.: Real-time object detection for smart vehicles. In: Intern. Conf. on Computer Vision, Corfu, Greece (1999), http://www.gavrila.net/Publications/./iccv99.pdf (accessed April 25, 2009)
  18. 18.
    Johnson, A.H., Hebert, M.: Recognizing objects by matching oriented points. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp. 684–689 (1997)Google Scholar
  19. 19.
    He, Y., Kundu, A.: 2D shape classification using hidden Markov model. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(11), 1172–1184 (1991)CrossRefGoogle Scholar
  20. 20.
    Liu, H.C., Srinath, M.D.: Partial shape classification using contour matching in distance transformation. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(11), 1072–1078 (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • A. Głowacz
    • 1
  • W. Głowacz
    • 1
  1. 1.Faculty of Electrical Engineering, Automatics, Computer Science and ElectronicsAGH University of Science and TechnologyKrakowPoland

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