Browsing by Subject "Dual-tree complex wavelet transform"
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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 Microscopic image classification via WT-based covariance descriptors using Kullback-Leibler distance(IEEE, 2012) Keskin, Furkan; Çetin, A. Enis; Erşahin, Tülin; Çetin-Atalay, RengülIn this paper, we present a novel method for classification of cancer cell line images using complex wavelet-based region covariance matrix descriptors. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a new region descriptor utilizing the dual-tree complex wavelet transform coefficients as pixel features is computed. WT as a feature extraction tool is preferred primarily because of its ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines, and approximate shift invariance property. We propose new dissimilarity measures between covariance matrices based on Kullback-Leibler (KL) divergence and L 2-norm, which turn out to be as successful as the classical KL divergence, but with much less computational complexity. Experimental results demonstrate the effectiveness of the proposed image classification framework. The proposed algorithm outperforms the recently published eigenvalue-based Bayesian classification method. © 2012 IEEE.Item Open Access Time-varying lifting structures for single-tree complexwavelet transform(IEEE, 2012) Keskin, Furkan; Çetin, A. EnisIn this paper, we describe a single-tree complex wavelet transform method using time-varying lifting structures. In the dualtree complex wavelet transform (DT-CWT), two different filterbanks are executed in parallel to analyze a given input signal, which increases the amount of data after analysis. DT-CWT leads to a redundancy factor of 2 d for d-dimensional signals. In the proposed single-tree complex wavelet transform (ST-CWT) structure, filters of the lifting filterbank switch back and forth between the two analysis filters of the DT-CWT. This approach does not increase the amount of output data as it is a critically sampled transform and it has the desirable properties of DT-CWT such as shift-invariance and directional selectivity. The proposed filterbank is capable of constructing a complex wavelet-like transform. Examples are presented. © 2012 IEEE.