Browsing by Author "Tevfik, A. H."

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Open Access
A signal representation approach for discrimination between full and empty hazelnuts
(IEEE, 2007) Onaran, İbrahim; İnce, N. F.; Tevfik, A. H.; Çetin, A. Enis
We apply a sparse signal representation approach to impact acoustic signals to discriminate between empty and full hazelnuts. The impact acoustic signals are recorded by dropping the hazelnut shells on a metal plate. The impact signal is then approximated within a given error limit by choosing codevectors from a special dictionary. This dictionary was generated from sub-dictionaries that are individually generated for the impact signals corresponding to empty and full hazelnut. The number of codevectors selected from each sub-dictionary and the approximation error within initial codevectors are used as classification features and fed to a Linear Discriminant Analysis (LDA). We also compare this algorithm with a baseline approach. This baseline approach uses features which describe the time and frequency characteristics of the given signal that were previously used for empty and full hazelnut separation. Classification accuracies of 98.3% and 96.8% were achieved by the proposed approach and base algorithm respectively. The results we obtained show that sparse signal representation strategy can be used as an alternative classification method for undeveloped hazelnut separation with higher accuracies.
Open Access
Subband coding of binary textual images for document retrieval
(IEEE, 1996) Gerek, Ömer N.; Çetin, A. Enis; Tevfik, A. H.
Efficient compression of binary textual images is very important for applications such as document archiving and retrieval, digital libraries and facsimile. The basic property of a textual image is the repetitions of small character images and curves inside the document. Exploiting the redundancy of these repetitions is the key step in most of the coding algorithms. In this paper, we use a similar compression method in subband domain. Four different subband decomposition schemes are described and their performances on textual image compression algorithm is examined. Experimentally, it is found that the described methods accomplish high compression ratios and they are suitable for fast database access and keyword search.