Polyphase adaptive filter banks for fingerprint image compression

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Polyphase adaptive filter banks for fingerprint image compression

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A perfect reconstruction polyphase filter bank structure is presented in which the filters adapt to the changing input conditions. The use of such a filter bank leads to higher compression results for images containing sharp edges such as fingerprint images.

Inspec keywords: least mean squares methods; filtering theory; image coding; data compression; adaptive filters; fingerprint identification

Other keywords: polyphase adaptive filter banks; perfect reconstruction filter bank structure; fingerprint image compression; sharp edges; LMS algorithm

Subjects: Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques; Information theory; Optical information, image and video signal processing; Interpolation and function approximation (numerical analysis); Codes; Pattern recognition

References

    1. 1)
      • I. Pitas , A. Venetsanopoulos . Adaptive filters based on order statistics. IEEE Trans. , 518 - 522
    2. 2)
      • I. Daubechies , W. Sweldens . Factoring wavelet transforms into lifting steps. J. Fourier Anal. Appl. , 3 , 247 - 269
    3. 3)
      • Gerek, Ö.N., Çetin, A.E.: `Linear/nonlinear adaptive polyphase subband decompositionstructures for image compression', IEEE Int. Conf. Acoustics, Speech, and Signal Processing,ICASSP'98, 12–15 May 1998, Seattle, WA.
    4. 4)
      • A. Said , W.A. Pearlman . An image multiresolution representation for lossless andlossy image compression. IEEE Trans. , 1303 - 1310
    5. 5)
      • P. Salembier , L. Jaquenoud , D. Gader . (1991) Adaptive morphological multiresolution decomposition, Image algebra and mathematical morphology.
    6. 6)
      • J.M. Shapiro . Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. , 12 , 3445 - 3462
    7. 7)
      • `WSQ gray-scale fingerprint image compression specification', IAFIS-IC-0110v2, February 1993.
    8. 8)
      • W. Sweldens , A.F. Laine , M. Unser . (1995) The lifting scheme: A new philosophy in biorthogonal wavelet constructions, Wavelet applications in signal and image processing III.
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