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A COMMON FEATURE REPRESENTATION FOR SPEECH FRAMES AND IMAGE CONTOURS

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Computer Vision and Graphics

Part of the book series: Computational Imaging and Vision ((CIVI,volume 32))

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Abstract

The technique of independent component analysis (ICA) in Fourier space is proposed for the detection of base functions, that can be applied both in speech coding and contour-based shape description in digital images. Our aim is to compare our coding scheme with the Mel cepstrum features of speech and the complex contour Fourier features of image contours. The basic advantage of ICA-based features is that they adapt to the available learning samples.

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REFERENCES

  1. W. Skarbek: Multimedia - Algorytmy i Standardy Kompresji. Akademicka Oficyna Wydawnicza PLJ, Warszawa, 1998.

    Google Scholar 

  2. H. Niemann H.: Pattern Analysis and Understanding. Springer, Berlin etc., 1990.

    Google Scholar 

  3. H. Kauppinen, T. Seppanen, M. Pietikainen M.: An Experimental Comparison of Autoregressive and Fourier-Based Descriptors in 2D Shape Classification. IEEE Trans. Patt. Anal. Mach. Intell., vol. 17 (1995), No. 2, 201–206.

    Google Scholar 

  4. I. Sekita, T. Kurita, N. Otsu: Complex autoregressive model for shape recognition. IEEE Trans. Patt. Anal. Mach. Intell., vol. 14 (1992), 489–496.

    Google Scholar 

  5. J.-C. Junqua, J.-P. Haton: Robustness in automatic speech recognition, Kluwer Academic Publications, Boston etc., 1996.

    Google Scholar 

  6. L. Rabiner, B. Juang: Fundamentals of Speech Recognition, Prentice Hall, 1993.

    Google Scholar 

  7. P. Ding, X. Kang, L. Zhang: Personal recognition using ICA, Proceedings ICONIP, 2001.

    Google Scholar 

  8. J. Rosca, A. Kofmehl: Cepstrum-like ICA representations for text independent speaker recognition, Procceedings of ICA'2003, (Nara, Japan, April 2003), NTT Kyoto, Japan.

    Google Scholar 

  9. A. Cichocki, S. Amari: Adaptive Blind Signal and Image Processing, John Wiley, Chichester, UK, 2002.

    Google Scholar 

  10. A. Hyvarinen, J. Karhunen, E. Oja: Independent Component Analysis, John Wiley & Sons, New York etc., 2001.

    Google Scholar 

  11. A.J. Bell, T.J. Sejnowski: The ‘independent components’ of natural scenes are edge filters. Vision Research, vol.37 (1997), 3327–3338.

    Article  Google Scholar 

  12. C. Liu, H. Wechsler: Independent Component Analysis of Gabor Features for Face Recognition. IEEE Transactions on Neural Networks, vol. 14 (2003), No. 4, 919–928.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Control and Computation Eng., Warsaw University of Technology, ul. Nowowiejska 15/19, Warszawa, 00-665, Poland

    Włodzimierz Kasprzak, Adam F. Okazaki & Robert Seta

Authors
  1. Włodzimierz Kasprzak
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  2. Adam F. Okazaki
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  3. Robert Seta
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Editor information

Editors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    K. Wojciechowski

  2. Silesian University of Technology, Gliwice, Poland

    B. Smolka

  3. Silesian University of Technology, Gliwice, Poland

    H. Palus

  4. The University of Western Australia, Crawley, Australia

    R.S. Kozera

  5. Warsaw University of Technology, Warsaw, Poland

    W. Skarbek

  6. The University of Western Australia, Crawley, Australia

    L. Noakes

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© 2006 Springer

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Kasprzak, W., F. Okazaki, A., Seta, R. (2006). A COMMON FEATURE REPRESENTATION FOR SPEECH FRAMES AND IMAGE CONTOURS. In: Wojciechowski, K., Smolka, B., Palus, H., Kozera, R., Skarbek, W., Noakes, L. (eds) Computer Vision and Graphics. Computational Imaging and Vision, vol 32. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4179-9_66

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  • DOI: https://doi.org/10.1007/1-4020-4179-9_66

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-4178-5

  • Online ISBN: 978-1-4020-4179-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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