Browsing by Subject "Mellin transform"

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    Mel-and Mellin-cepstral feature extraction algorithms for face recognition
    (Oxford University Press, 2011-01-17) Cakir, S.; Çetin, A. Enis
    In this article, an image feature extraction method based on two-dimensional (2D) Mellin cepstrum is introduced. The concept of one-dimensional (1D) mel-cepstrum that is widely used in speech recognition is extended to two-dimensions using both the ordinary 2D Fourier transform and the Mellin transform. The resultant feature matrices are applied to two different classifiers such as common matrix approach and support vector machine to test the performance of the mel-cepstrum- and Mellin-cepstrum-based features. The AR face image database, ORL database, Yale database and FRGC database are used in experimental studies, which indicate that recognition rates obtained by the 2D mel-cepstrum-based method are superior to that obtained using 2D principal component analysis, 2D Fourier-Mellin transform and ordinary image matrix-based face recognition in both classifiers. Experimental results indicate that 2D cepstral analysis can also be used in other image feature extraction problems. © The Author 2010. Published by Oxford University Press on behalf of The British Computer Society.
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    Two-dimensional Mellin and mel-cepstrum for image feature extraction
    (Springer, Dordrecht, 2010) Çakır, Serdar; Çetin, A. Enis
    An image feature extraction method based on two-dimensional (2D)Mellin cepstrum is introduced. The concept of one-dimensional (1D) melcepstrum which is widely used in speech recognition is extended to two-dimensions both using the ordinary 2D Fourier Transform and the Mellin transform in this article. The resultant feature matrices are applied to two different classifiers (Common Matrix Approach and Support Vector Machine) to test the performance of the melcepstrum and Mellincepstrum based features. Experimental studies indicate that recognition rates obtained by the 2D melcepstrum based method are superior to the recognition rates obtained using 2D PCA and ordinary image matrix based face recognition in both classifiers. © 2011 Springer Science+Business Media B.V.