• Acta Optica Sinica
  • Vol. 20, Issue 7, 912 (2000)
[in Chinese], [in Chinese], [in Chinese], and [in Chinese]
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    [in Chinese], [in Chinese], [in Chinese], [in Chinese]. Optical Distortion-Invariant Image Recognition Based on the Multivariated Discriminant Analysis[J]. Acta Optica Sinica, 2000, 20(7): 912 Copy Citation Text show less
    References

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    [3] Liu H, Wu M, Jin G et al.. An automatic human face recognition system. Opt. Lasers in Engng., 1998, 30(3, 4):305~314

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    [7] Swets D L, Weng J. Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell., 1996, 18(8):831~836

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    [10] Pentland A, Moghaddam B, Starner T. View-based and modular eigenspaces for face recognition. Proc. IEEE Computer Soc. Conf. on Computer Vision and Patt. Recog., 1994, 84~91

    [11] Vijaya Kumar B V K, Casasent D, Murakami H. Principle-component imagery for statistical pattern recognition correlators. Opt. Engng., 1982, 21(1):43~47

    [12] Merkle F, Lorch T. Hybrid optical-digital pattern recognition. Appl. Opt., 1984, 23(10):1509~1516

    [14] Fukunaga K. Introduction to Statistical Pattern Recognition. second edition, San Diego: Academic Press, 1990. 399~450

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    [in Chinese], [in Chinese], [in Chinese], [in Chinese]. Optical Distortion-Invariant Image Recognition Based on the Multivariated Discriminant Analysis[J]. Acta Optica Sinica, 2000, 20(7): 912
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