Browsing by Author "Çakir, S."

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    Mel-cepstral feature extraction methods for image representation
    (S P I E - International Society for Optical Engineering, 2010-15-09) Çakir, S.; Çetin, A. Enis
    An image feature extraction method based on the twodimensional (2-D) mel cepstrum is introduced. The concept of onedimensional mel cepstrum, which is widely used in speech recognition, is extended to 2-D in this article. The feature matrix resulting from the 2-D mel-cepstral analysis are applied to the support-vector-machine classifier with multi-class support to test the performance of the mel-cepstrum feature matrix. The AR, ORL, and Yale face databases are used in experimental studies, which indicate that recognition rates obtained by the 2-D mel-cepstrum method are superior to the recognition rates obtained using 2-D principal-component analysis and ordinary image-matrixbased face recognition. Experimental results show that 2-D mel-cepstral analysis can also be used in other image feature extraction problems. .
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    Region covariance descriptors calculated over the salient points for target tracking
    (IEEE, 2012) Çakir, S.; Aytaç, T.; Yildirim, A.; Beheshti, S.; Gerek Ö.N.; Çetin, A. Enis
    Features extracted at salient points in the image are used to construct region covariance descriptor (RCD) for target tracking purposes. In the classical approach, the RCD is computed by using the features at each pixel location and thus, increases the computational cost in the scenarios where large targets are tracked. The approach in which the features at each pixel location are used, is redundant in cases where image statistics do not change significantly between neighboring pixels. Furthermore, this may decrease the tracking accuracy while tracking large targets which have background dominating structures. In the proposed approach, the salient points are extracted via the Shi and Tomasi's minimum eigenvalue method and a descriptor based target tracking structure is constructed based on the features extracted only at these salient points. Experimental results indicate that the proposed method provides comparable and in some cases even better tracking results compared to the classical method while providing a computationally more efficient structure. © 2012 IEEE.