Browsing by Subject "Principal component analysis (PCA)"

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    Classification of closed and open shell pistachio nuts using principal component analysis of impact acoustics
    (IEEE, 2004-05) Çetin, A. Enis; Pearson, T. C.; Tewfik, A. H.
    An algorithm was developed to separate pistachio nuts with closed-shells from those with open-shells. It was observed that upon impact on a steel plate, nuts with closed-shells emit different sounds than nuts with open-shells. Two feature vectors extracted from the sound signals were melcepstrum coefficients and eigenvalues obtained from the principle component analysis of the autocorrelation matrix of the signals. Classification of a sound signal was done by linearly combining feature vectors from both mel-cepstrum and PCA feature vectors. An important property of the algorithm is that it is easily trainable. During the training phase, sounds of the nuts with closed-shells and open-shells were used to obtain a representative vector of each class. The accuracy of closed-shell nuts was more than 99% on the test set.
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    Target detection and classification in SAR images using region covariance and co-difference
    (SPIE, 2009-04) Duman, Kaan; Eryıldırım, Abdulkadir; Çetin, A. Enis
    In this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed. The new approach is based on region covariance (RC) method which involves the computation of a covariance matrix whose entries are used in target detection and classification. In addition the region co-difference matrix is also introduced. Experimental results of object detection in MSTAR (moving and stationary target recognition) database are presented. The RC and region co-difference method delivers high detection accuracy and low false alarm rates. It is also experimentally observed that these methods produce better results than the commonly used principal component analysis (PCA) method when they are used with different distance metrics introduced. © 2009 SPIE.