Browsing by Author "Cetin, A.E."
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Item Open Access Classifying fonts and calligraphy styles using complex wavelet transform(Springer-Verlag London Ltd, 2015) Bozkurt, A.; Duygulu P.; Cetin, A.E.Recognizing fonts has become an important task in document analysis, due to the increasing number of available digital documents in different fonts and emphases. A generic font recognition system independent of language, script and content is desirable for processing various types of documents. At the same time, categorizing calligraphy styles in handwritten manuscripts is important for paleographic analysis, but has not been studied sufficiently in the literature. We address the font recognition problem as analysis and categorization of textures. We extract features using complex wavelet transform and use support vector machines for classification. Extensive experimental evaluations on different datasets in four languages and comparisons with state-of-the-art studies show that our proposed method achieves higher recognition accuracy while being computationally simpler. Furthermore, on a new dataset generated from Ottoman manuscripts, we show that the proposed method can also be used for categorizing Ottoman calligraphy with high accuracy. © 2015, Springer-Verlag London.Item Open Access A multiplication-free framework for signal processing and applications in biomedical image analysis(IEEE, 2013) Suhre, A.; Keskin F.; Ersahin, T.; Cetin-Atalay, R.; Ansari, R.; Cetin, A.E.A new framework for signal processing is introduced based on a novel vector product definition that permits a multiplier-free implementation. First a new product of two real numbers is defined as the sum of their absolute values, with the sign determined by product of the hard-limited numbers. This new product of real numbers is used to define a similar product of vectors in RN. The new vector product of two identical vectors reduces to a scaled version of the l1 norm of the vector. The main advantage of this framework is that it yields multiplication-free computationally efficient algorithms for performing some important tasks in signal processing. An application to the problem of cancer cell line image classification is presented that uses the notion of a co-difference matrix that is analogous to a covariance matrix except that the vector products are based on our new proposed framework. Results show the effectiveness of this approach when the proposed co-difference matrix is compared with a covariance matrix. © 2013 IEEE.Item Open Access A Wi-Fi cluster based wireless sensor network application and deployment for wildfire detection(Hindawi Publishing Corporation, 2014) Ulucinar, A.R.; Korpeoglu I.; Cetin, A.E.We introduce the wireless sensor network (WSN) data harvesting application we developed for wildfire detection and the experiments we have performed. The sensor nodes are equipped with temperature and relative humidity sensors. They are organized into clusters and they communicate with the cluster heads using 802.15.4/ZigBee wireless links. The cluster heads report the harvested data to the control center using 802.11/Wi-Fi links. We introduce the hardware and the software architecture of our deployment near Rhodiapolis, an ancient city raising on the outskirts of Kumluca county of Antalya, Turkey. We detail our technical insights into the deployment based on the real-world data collected from the site. We also propose a temperature-based fire detection algorithm and we evaluate its performance by performing experiments in our deployment site and also in our university. We observed that our WSN application can reliably report temperature data to the center quickly and our algorithms can detect fire events in an acceptable time frame with no or very few false positives. © 2014 Alper Rifat Ulucinar et al.