Browsing by Author "Kose, K."

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Open Access
Compressive sensing using the modified entropy functional
(Academic Press, 2014-01) Kose, K.; Gunay, O.; Çetin, A. Enis
In most compressive sensing problems, 1 norm is used during the signal reconstruction process. In this article, a modified version of the entropy functional is proposed to approximate the 1 norm. The proposed modified version of the entropy functional is continuous, differentiable and convex. Therefore, it is possible to construct globally convergent iterative algorithms using Bregman’s row-action method for compressive sensing applications. Simulation examples with both 1D signals and images are presented. © 2013 Elsevier Inc. All rights reserved.
Open Access
Denoising images corrupted by impulsive noise using projections onto the epigraph set of the total variation function (PES-TV)
(Springer U K, 2015) Tofighi M.; Kose, K.; Çetin, A. Enis
In this article, a novel algorithm for denoising images corrupted by impulsive noise is presented. Impulsive noise generates pixels whose gray level values are not consistent with the neighboring pixels. The proposed denoising algorithm is a two-step procedure. In the first step, image denoising is formulated as a convex optimization problem, whose constraints are defined as limitations on local variations between neighboring pixels. We use Projections onto the Epigraph Set of the TV function (PES-TV) to solve this problem. Unlike other approaches in the literature, the PES-TV method does not require any prior information about the noise variance. It is only capable of utilizing local relations among pixels and does not fully take advantage of correlations between spatially distant areas of an image with similar appearance. In the second step, a Wiener filtering approach is cascaded to the PES-TV-based method to take advantage of global correlations in an image. In this step, the image is first divided into blocks and those with similar content are jointly denoised using a 3D Wiener filter. The denoising performance of the proposed two-step method was compared against three state-of-the-art denoising methods under various impulsive noise models.
Open Access
Entropy-functional-based online adaptive decision fusion framework with application to wildfire detection in video
(IEEE, 2012-01-09) Gunay, O.; Toreyin, B. U.; Kose, K.; Çetin, A. Enis
In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented.
Open Access
Image classification of human carcinoma cells using complex wavelet-based covariance descriptors
(Public Library of Science, 2013-01-16) Keskin, F.; Suhre, A.; Kose, K.; Ersahin, T.; Çetin, A. Enis; Cetin Atalay, R.
Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-CWT) coefficients and several morphological attributes are computed. Directionally selective DT-CWT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time-and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.
Open Access
Low-Pass Filtering of Irregularly Sampled Signals Using a Set Theoretic Framework
(IEEE, 2011-06-15) Kose, K.; Çetin, A. Enis
In this article, the goal is to show that it is possible to filter nonuniformly sampled signals according to specs defined in the Fourier domain. In many practical applications, it is necessary to filter irregularly sampled data including seismic signal processing, synthetic aperture radar (SAR) imaging systems, three-dimensional (3-D) meshes, and digital terrain models [1], [2].
Open Access
Special issue on microscopic image processing
(Springer U K, 2014) Kose, K.; Cetin Atalay, R.; Çetin, A. Enis
Open Access
Video-Based FLame detection for the protection of cultural heritage
(SAGE, 2013) Dimitropoulos, K.; Gunay, O.; Kose, K.; Erden, F.; Chaabane, F.; Tsalakanidou, F.; Grammalidis, N.; Çetin, A. Enis
The majority of cultural heritage and archaeological sites, especiallyin the Mediterranean region, are covered with vegetation, whichincreases the risk of fires. These fires may also break out and spreadtowards nearby forests and other wooded land, or conversely start innearby forests and spread to archaeological sites. Beyond takingprecautionary measures to avoid a forest fire, early warning andimmediate response to a fire breakout are the only ways to avoidgreat losses and environmental and cultural heritage damages. Theuse of terrestrial systems, typically based on video cameras, iscurrently the most promising solution for advanced automatic wildfiresurveillance and monitoring due to its low cost and short responsetime. Early and accurate detection and localization of flame is anessential requirement of these systems, however, it remains achallenging issue due to the fact that many natural objects havesimilar characteristics with fire. This paper presents and comparesthree video-based flame detection techniques, which weredeveloped within the FIRESENSE EU research project, taking intoaccount the chaotic and complex nature of the fire phenomenonand the large variations of flame appearance in video. Experimentalresults show that the proposed methods provide high fire detectionrates with reasonable false alarm ratios.
Open Access
Wavelet based flickering flame detector using differential PIR sensors
(Elsevier, 2012-07-06) Erden, F.; Toreyin, B. U.; Soyer, E. B.; Inac, I.; Gunay, O.; Kose, K.; Çetin, A. Enis
A Pyro-electric Infrared (PIR) sensor based flame detection system is proposed using a Markovian decision algorithm. A differential PIR sensor is only sensitive to sudden temperature variations within its viewing range and it produces a time-varying signal. The wavelet transform of the PIR sensor signal is used for feature extraction from sensor signal and wavelet parameters are fed to a set of Markov models corresponding to the flame flicker process of an uncontrolled fire, ordinary activity of human beings and other objects. The final decision is reached based on the model yielding the highest probability among others. Comparative results show that the system can be used for fire detection in large rooms.