Browsing by Author "Çetin, A. Enis"

Now showing 1 - 20 of 270

Open Access
2-D adaptive prediction based Gaussianity tests in microcalcification detection
(SPIE, 1998-01) Gürcan, M. Nafi; Yardımcı, Yasemin; Çetin, A. Enis
With increasing use of Picture Archiving and Communication Systems (PACS), Computer-aided Diagnosis (CAD) methods will be more widely utilized. In this paper, we develop a CAD method for the detection of microcalcification clusters in mammograms, which are an early sign of breast cancer. The method we propose makes use of two-dimensional (2-D) adaptive filtering and a Gaussianity test recently developed by Ojeda et al. for causal invertible time series. The first step of this test is adaptive linear prediction. It is assumed that the prediction error sequence has a Gaussian distribution as the mammogram images do not contain sharp edges. Since microcalcifications appear as isolated bright spots, the prediction error sequence contains large outliers around microcalcification locations. The second step of the algorithm is the computation of a test statistic from the prediction error values to determine whether the samples are from a Gaussian distribution. The Gaussianity test is applied over small, overlapping square regions. The regions, in which the Gaussianity test fails, are marked as suspicious regions. Experimental results obtained from a mammogram database are presented.
Open Access
A 2-D orientation-adaptive prediction filter in lifting structures for image coding
(Institute of Electrical and Electronics Engineers, 2006) Gerek, Ö. N.; Çetin, A. Enis
Lifting-style implementations of wavelets are widely used in image coders. A two-dimensional (2-D) edge adaptive lifting structure, which is similar to Daubechies 5/3 wavelet, is presented. The 2-D prediction filter predicts the value of the next polyphase component according to an edge orientation estimator of the image. Consequently, the prediction domain is allowed to rotate ±45° in regions with diagonal gradient. The gradient estimator is computationally inexpensive with additional costs of only six subtractions per lifting instruction, and no multiplications are required.
Open Access
3-Boyutlu orman yangını yayılımı sistemi
(IEEE, 2008) Köse, Kıvanç; Yılmaz, E.; Grammalidis, N.; Aktuğ, B.; Çetin, A. Enis; Aydın, İ.
In the last few years, due to the global warming and draught related to it, there is an increase in the number of forest fires. Forest fire detection is mainly done by people but there exists some automated systems in this field too. Besides the detection of the forest fires, effective fire extinhguising has an important role in fire fighting. If the spread of the fire can be predicted from the starting, early intervene can be achieved and fire can be extinguished swiftly. Using the Fire Propagation Simulator explained here it is aimed, to predict the fire development beforehand and to visulalize this predictions on a 3D-GIS environment. ©2008 IEEE.
Open Access
3D forest fire propagation simulation
(IEEE, 2008-05) Köse, Kıvanç; Grammalidis, N.; Yılmaz, E.; Çetin, A. Enis
The increase in the number of forest fires in the last few years dispatch governments to take precautions. Besides prevention, early intervention is also very important in fire fighting. If the firefighters know where the fire will be in some time, it would be easier for them to stop the fire. Therefore a big need for simulating the fire behavior exists. In this paper we are proposing a system which can simulate the propagation of fire in time. Also this system can visualize the propagation of fire in any 3D-GIS environment, that accepts KMZ as a file format. Besides, any user demanded data can be visualized on the map of the system. This gives the chance of fire planning to firefighters. The system can visualize its results on 3D screens in 3D. Therefore, a better understanding of the terrain can be obtained. ©2008 IEEE.
Open Access
3D model compression using connectivity-guided adaptive wavelet transform built into 2D SPIHT
(Academic Press, 2010-01) Köse K.; Çetin, A. Enis; Güdükbay, Uğur; Onural, L.
Connectivity-Guided Adaptive Wavelet Transform based mesh compression framework is proposed. The transformation uses the connectivity information of the 3D model to exploit the inter-pixel correlations. Orthographic projection is used for converting the 3D mesh into a 2D image-like representation. The proposed conversion method does not change the connectivity among the vertices of the 3D model. There is a correlation between the pixels of the composed image due to the connectivity of the 3D mesh. The proposed wavelet transform uses an adaptive predictor that exploits the connectivity information of the 3D model. Known image compression tools cannot take advantage of the correlations between the samples. The wavelet transformed data is then encoded using a zero-tree wavelet based method. Since the encoder creates a hierarchical bitstream, the proposed technique is a progressive mesh compression technique. Experimental results show that the proposed method has a better rate distortion performance than MPEG-3DGC/MPEG-4 mesh coder.
Open Access
3DTV: Capture, transmission, and Display of 3D Video
(2009) Triantafyllidis, G. A.; Çetin, A. Enis; Smolic, A.; Onural, L.; Sikora, T.; Watson, J.
[No abstract available]
Open Access
Adaptive decision fusion based cooperative spectrum sensing for cognitive radio systems
(IEEE, 2011) Töreyin, B. U.; Yarkan, S.; Qaraqe, K. A.; Çetin, A. Enis
In this paper, an online Adaptive Decision Fusion (ADF) framework is proposed for the central spectrum awareness engine of a spectrum sensor network in Cognitive Radio (CR) systems. Online learning approaches are powerful tools for problems where drifts in concepts take place. Cooperative spectrum sensing in cognitive radio networks is such a problem where channel characteristics and utilization patterns change frequently. The importance of this problem stems from the requirement that secondary users must adjust their frequency utilization strategies in such a way that the communication performance of the primary users would not be degraded by any means. In the proposed framework, sensing values from several sensor nodes are fused together by weighted linear combination at the central spectrum awareness engine. The weights are updated on-line according to an active fusion method based on performing orthogonal projections onto convex sets describing power reading values from each sensor. The proposed adaptive fusion strategy for cooperative spectrum sensing can operate independent from the channel type between the primary user and secondary users. Results of simulations and experiments for the proposed method conducted in laboratory are also presented. © 2011 IEEE.
Open Access
Adaptive filtering approaches for non-Gaussian stable processes
(IEEE, 1995-05) Arıkan, Orhan; Belge, Murat; Çetin, A. Enis; Erzin, Engin
A large class of physical phenomenon observed in practice exhibit non-Gaussian behavior. In this paper, α-stable distributions, which have heavier tails than Gaussian distribution, are considered to model non-Gaussian signals. Adaptive signal processing in the presence of such kind of noise is a requirement of many practical problems. Since, direct application of commonly used adaptation techniques fail in these applications, new approaches for adaptive filtering for α-stable random processes are introduced.
Open Access
Adaptive filtering for non-gaussian stable processes
(IEEE, 1994) Arıkan, Orhan; Çetin, A. Enis; Erzin, E.
A large class of physical phenomenon observed in practice exhibit non-Gaussian behavior. In this letter, a-stable distributions, which have heavier tails than Gaussian distribution, are considered to model non-Gaussian signals. Adaptive signal processing in the presence of such a noise is a requirement of many practical problems. Since direct application of commonly used adaptation techniques fail in these applications, new algorithms for adaptive filtering for α-stable random processes are introduced.
Open Access
Adaptive methods for dithering color images
(SPIE Optical Engineering Press, 1995) Akarun, L.; Yardımcı, Y.; Çetin, A. Enis; Allebach, J. P.
Open Access
Adaptive methods for dithering color images
(Institute of Electrical and Electronics Engineers, 1997-07) Akarun, L.; Yardımcı, Y.; Çetin, A. Enis
Most color image printing and display devices do not have the capability of reproducing true color images. A common remedy is the use of dithering techniques that take advantage of the lower sensitivity of the eye to spatial resolution and exchange higher color resolution with lower spatial resolution. In this paper, an adaptive error diffusion method for color images is presented. The error diffusion filter coefficients are updated by a normalized least mean square-type (LMS-type) algorithm to prevent textural contours, color impulses, and color shifts, which are among the most common side effects of the standard dithering algorithms. Another novelty of the new method is its vector character: Previous applications of error diffusion have treated the individual color components of an image separately. Here, we develop a general vector approach and demonstrate through simulation studies that superior results are achieved.
Open Access
Adaptive polyphase subband decomposition structures for image compression
(IEEE, 2000) Gerek, Ö. N.; Çetin, A. Enis
Subband decomposition techniques have been extensively used for data coding and analysis. In most filter banks, the goal is to obtain subsampled signals corresponding to different spectral regions of the original data. However, this approach leads to various artifacts in images having spatially varying characteristics, such as images containing text, subtitles, or sharp edges. In this paper, adaptive filter banks with perfect reconstruction property are presented for such images. The filters of the decomposition structure which can be either linear or nonlinear vary according to the nature of the signal. This leads to improved image compression ratios. Simulation examples are presented.
Open Access
Algebraic error analysis of collinear feature points for camera parameter estimation
(Elsevier, 2011-01-04) Urfalioglu, O.; Thormählen, T.; Broszio, H.; Mikulastik, P.; Çetin, A. Enis
In general, feature points and camera parameters can only be estimated with limited accuracy due to noisy images. In case of collinear feature points, it is possible to benefit from this geometrical regularity by correcting the feature points to lie on the supporting estimated straight line, yielding increased accuracy of the estimated camera parameters. However, regarding Maximum-Likelihood (ML) estimation, this procedure is incomplete and suboptimal. An optimal solution must also determine the error covariance of corrected features. In this paper, a complete theoretical covariance propagation analysis starting from the error of the feature points up to the error of the estimated camera parameters is performed. Additionally, corresponding Fisher Information Matrices are determined and fundamental relationships between the number and distance of collinear points and corresponding error variances are revealed algebraically. To demonstrate the impact of collinearity, experiments are conducted with covariance propagation analyses, showing significant reduction of the error variances of the estimated parameters.
Open Access
AM/FM signal estimation with micro-segmentation and polynomial fit
(Springer U K, 2014-03) Deprem, Z.; Çetin, A. Enis; Arıkan, Orhan
Amplitude and phase estimation of AM/FM signals with parametric polynomial representation require the polynomial orders for phase and amplitude to be known. But in reality, they are not known and have to be estimated. A well-known method for estimation is the higher-order ambiguity function (HAF) or its variants. But the HAF method has several reported drawbacks such as error propagation and slowly varying or even constant amplitude assumption. Especially for the long duration time-varying signals like AM/FM signals, which require high orders for the phase and amplitude, computational load is very heavy due to nonlinear optimization involving many variables. This paper utilizes a micro-segmentation approach where the length of segment is selected such that the amplitude and instantaneous frequency (IF) is constant over the segment. With this selection first, the amplitude and phase estimates for each micro-segment are obtained optimally in the LS sense, and then, these estimates are concatenated to obtain the overall amplitude and phase estimates. The initial estimates are not optimal but sufficiently close to the optimal solution for subsequent processing. Therefore, by using the initial estimates, the overall polynomial orders for the amplitude and phase are estimated. Using estimated orders, the initial amplitude and phase functions are fitted to the polynomials to obtain the final signal. The method does not use any multivariable nonlinear optimization and is efficient in the sense that the MSE performance is close enough to the Cramer–Rao bound. Simulation examples are presented.
Open Access
Approximate computation of DFT without performing any multiplications: application to radar signal processing
(IEEE, 2014) Arslan, Musa Tunç; Bozkurt, Alican; Sevimli, Rasim Akın; Akbaş, Cem Emre; Çetin, A. Enis
In many radar problems it is not necessary to compute the ambiguity function in a perfect manner. In this article a new multiplication free algorithm for approximate computation of the ambiguity function is introduced. All multiplications (a × b) in the ambiguity function are replaced by an operator which computes sign(a × b)(a + b). The new transform is especially useful when the signal processing algorithm requires correlations. Ambiguity function in radar signal processing requires high number of correlations and DFT computations. This new additive operator enables an approximate computation of the ambiguity function without requiring any multiplications. Simulation examples involving passive radars are presented.
Open Access
Automated cancer stem cell recognition in H and E stained tissue using convolutional neural networks and color deconvolution
(SPIE, 2017) Aichinger, W.; Krappe, S.; Çetin, A. Enis; Çetin-Atalay, R.; Üner, A.; Benz, M.; Wittenberg, T.; Stamminger, M.; Münzenmayer, C.
The analysis and interpretation of histopathological samples and images is an important discipline in the diagnosis of various diseases, especially cancer. An important factor in prognosis and treatment with the aim of a precision medicine is the determination of so-called cancer stem cells (CSC) which are known for their resistance to chemotherapeutic treatment and involvement in tumor recurrence. Using immunohistochemistry with CSC markers like CD13, CD133 and others is one way to identify CSC. In our work we aim at identifying CSC presence on ubiquitous Hematoxilyn and Eosin (HE) staining as an inexpensive tool for routine histopathology based on their distinct morphological features. We present initial results of a new method based on color deconvolution (CD) and convolutional neural networks (CNN). This method performs favorably (accuracy 0.936) in comparison with a state-of-the-art method based on 1DSIFT and eigen-analysis feature sets evaluated on the same image database. We also show that accuracy of the CNN is improved by the CD pre-processing.
Open Access
Automated detection and enhancement of microcalcifications in mammograms using nonlinear subband decomposition
(IEEE, 1997) Ansari, R.; Gürcan, M. Nafi; Yardımcı, Yasemin; Çetin, A. Enis
In this paper, computer-aided detection and enhancement of microcalcifications in mammogram images are considered. The mammogram image is first decomposed into subimages using a `subband' decomposition filter bank which uses nonlinear filters. A suitably identified subimage is divided into overlapping square regions in which skewness and kurtosis as measures of the asymmetry and impulsiveness of the distribution are estimated. All regions with high positive skewness and kurtosis are marked as a regions of interest. Next, an outlier labeling method is used to find the locations of microcalcifications in these regions. An enhanced mammogram image is also obtained by emphasizing the microcalcification locations. Linear and nonlinear subband decomposition structures are compared in terms of their effectiveness in finding microcalcificated regions and their computational complexity. Simulation studies based on real mammogram images are presented.
Open Access
Baǧlanırlıkla yönlendirilmiş uyarlamalı dalgacık dönüşümü ile üç boyutlu model sıkıştırılması
(IEEE, 2007-06) Köse, Kıvanç; Çetin, A. Enis; Güdükbay, Uğur; Onural, Levent
Dikdörtgensel olmayan dalgacık dönüşümüne dayalı çok çözünürlüklü üç boyutlu model sıkıştırılması için iki yöntem önerilmektedir. Bunlar Sıradüzensel Ağaç Yapılarının Kümelere Bölütlenmesi (Set Partitioning In Hierarchical Trees - SPIHT) ve JPEG2000 tekniklerine dayanmaktadır. Üç boyutlu modeller düzenli ızgara yapılar üzerinde tanımlı iki boyutlu imgelere dönüştürülmekte, ve bu gösterim bağlanırlıkla yönlendirilmiş uyarlamalı dalgacık dönüşümünden geçirilerek ortaya çıkan dalgacık kümesi verisi SPITH veya JPEG2000 yöntemlerinden biri uygulanarak bit dizgisine dönüştürülmektedir. SPIHT ile elde edilen bit dizgisinin değişik uzunluklardaki bölümlerinden modelin değişik çözünürlüklerde geri çatmak mümkün olduğundan önerilen bu yöntem modellerin aşamalı gösterimine olanak sağlamaktadır. Dalgacık dönüşümü verilerinin SPIHT ile kodlanmasıyla elde edilen sonuç JPEG2000 ve MPEG-3DGC ile yapılan kodlamanın sonucundan daha başarılı olmuştur. Two compression frameworks that are based on a Set Partitioning In Hierarchical Trees (SPIHT) and JPEG2000 methods are proposed. The 3D mesh is first transformed to 2D images on a regular grid structure. Then, this image-like representation is wavelet transformed employing an adaptive predictor that takes advantage of the connectivity information of mesh vertices. Then SPIHT or JPEG2000 is applied on the wavelet domain data. The SPIHT based method is progressive because the resolution of the reconstructed mesh can be changed by varying the length of the one-dimensional data stream created by SPIHT algorithm. The results of the SPIHT based algorith is observed to be superior to JPEG200 based mesh coder and MPEG-3DGC in rate-distortion.
Open Access
Bandwidth selection for kernel density estimation using fourier domain constraints
(Institution of Engineering and Technology, 2016) Suhre, A.; Arıkan, Orhan; Çetin, A. Enis
Kernel density estimation (KDE) is widely-used for non-parametric estimation of an underlying density from data. The performance of KDE is mainly dependent on the bandwidth parameter of the kernel. This study presents an alternative method of estimating the bandwidth by incorporating sparsity priors in the Fourier transform domain. By using cross-validation (CV) together with an l1 constraint, the proposed method significantly reduces the under-smoothing effect of traditional CV methods. A solution for all free parameters in the minimisation is proposed, such that the algorithm does not need any additional parameter tuning. Simulation results indicate that the new approach is able to outperform classical and more recent approaches over a set of distributions of interest.
Open Access
Binary morphological subband decomposition for image coding
(IEEE, 1996) Gürcan, Metin Nafi; Gerek, Ömer Nezih; Çetin, A. Enis
In this paper a binary waveform coding method based on morphological subband decomposition coupled with embedded zero-tree and entropy coding is described. This method can be utilized in text compression or bit-plane coding of images. Binary morphological subband decomposition operations are carried out in the Gallois Field, resulting in a computationally efficient structure. Simulation studies are presented.