Abstract
A fast objects detection method is proposed, which is based on the variance-maximization learning of lifting dyadic wavelet filters. First, we derive a difference equation from two kinds of lifting high-pass components of a target image. The difference equation is an approximation of an inverse problem of an elliptic equation, which includes free parameters of the lifting filter. Since this discrete inverse problem is ill-conditioned, the free parameters are learned by using the least square method and a regularization method. Objects detection is done by applying the learned lifting filter to a query image.
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References
Abdukirim Turki, T., Niijima, K., Takano, S.: Design of bi-orthogonal wavelet filters using dyadic lifting scheme. Bulletin of Informatics and Cybernetics 37(1), 123–136 (2005)
Wiskott, L., Fellous, J.M., Krfiger, N., vonder Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Transaction on PAMI 19(7), 775–779 (1997)
Turk, M., Pentland, A.: Eigen faces for recognition. Journal of Congnitive Neuroscience 3(1), 71–86 (1991)
Burel, G., Carel, D.: Detection and localization of faces on digital images. Pattern Recognition Letters 15(10), 963–967 (1994)
Osuna, E., Freund, R., Girosi, F.: Training support vector machines: An application to face detection. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 130–136 (1997)
Sweldens, W.: The lifting scheme: A custom-design construction of biorthogonal wavelets. Appl. Comput. Harmon. Anal. 3(2), 186–200 (1996)
Abdukirim Turki, T., Hussain, M., Niijima, K., Takano, S.: The dyadic lifting schemes and the de-noising of digital image. International Journal of Wavelets, Multi-resolution and Information Processing 6(3), 331–351 (2008)
Abdukirim Turki, T.: Lifting Dyadic Wavelet Theory and Design of Filters for Image Processing. Ph.D Thesis, Kyushu University (February 2005)
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© 2014 Springer-Verlag Berlin Heidelberg
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Abulikemu, A., Yushan, A., Turki, T.A., Osman, A. (2014). Objects Detection Method by Learning Lifted Wavelet Filters. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_42
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DOI: https://doi.org/10.1007/978-3-662-45646-0_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45645-3
Online ISBN: 978-3-662-45646-0
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