Browsing by Subject "Covariance matrix"
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Item Open Access Average error in recovery of sparse signals and discrete fourier transform(IEEE, 2012-04) Özçelikkale, Ayça; Yüksel, S.; Özaktaş Haldun M.In compressive sensing framework it has been shown that a sparse signal can be successfully recovered from a few random measurements. The Discrete Fourier Transform (DFT) is one of the transforms that provide the best performance guarantees regardless of which components of the signal are nonzero. This result is based on the performance criterion of signal recovery with high probability. Whether the DFT is the optimum transform under average error criterion, instead of high probability criterion, has not been investigated. Here we consider this optimization problem. For this purpose, we model the signal as a random process, and propose a model where the covariance matrix of the signal is used as a measure of sparsity. We show that the DFT is, in general, not optimal despite numerous results that suggest otherwise. © 2012 IEEE.Item Open Access Counting surrounding nodes using DS-SS signals and de Bruijn sequences in blind environment(IEEE, 2013-03) Warty, C.; Seçer, Görkem; Yu, R.W.; Spinsante, S.In recent years the technological development has encouraged several applications based on node to node communications without any fixed infrastructure. This paper presents preliminary evaluation of popular estimating techniques to populate active nodes in the neighborhood using De Bruijn sequences. They have much higher cardinality compared to any other family of binary sequences at a parity of length. This characteristic of De Bruijn sequences can be exploited to identify the presence of an active node in a dense surrounding, to assist the primary node in making intelligent decisions in a blind or foggy environment. The simulation model in this paper evaluates the use of eigenvalue estimation to estimate the spreading sequence among noisy signals, based on eigenvalues analysis techniques. The received signal is divided into windows, from which a covariance matrix is computed; the sequence can be reconstructed from the two first eigenvectors of this matrix, and that useful information, such as the desynchronization time, can be extracted from the eigenvalues. © 2013 IEEE.Item Open Access Covariance matrix-based fire and flame detection method in video(Springer, 2011-09-17) Habiboğlu, Y. H.; Günay, O.; Çetin, A. EnisThis paper proposes a video-based fire detection system which uses color, spatial and temporal information. The system divides the video into spatio-temporal blocks and uses covariance-based features extracted from these blocks to detect fire. Feature vectors take advantage of both the spatial and the temporal characteristics of flame-colored regions. The extracted features are trained and tested using a support vector machine (SVM) classifier. The system does not use a background subtraction method to segment moving regions and can be used, to some extent, with non-stationary cameras. The computationally efficient method can process 320×240 video frames at around 20 frames per second in an ordinary PC with a dual core 2.2 GHz processor. In addition, it is shown to outperform a previous method in terms of detection performance.Item Open Access Detection of fungal damaged popcorn using image property covariance features(Elsevier, 2012) Yorulmaz, O.; Pearson, T. C.; Çetin, A.Covariance-matrix-based features were applied to the detection of popcorn infected by a fungus that causes a symptom called " blue-eye" . This infection of popcorn kernels causes economic losses due to the kernels' poor appearance and the frequently disagreeable flavor of the popped kernels. Images of kernels were obtained to distinguish damaged from undamaged kernels using image-processing techniques. Features for distinguishing blue-eye-damaged from undamaged popcorn kernel images were extracted from covariance matrices computed using various image pixel properties. The covariance matrices were formed using different property vectors that consisted of the image coordinate values, their intensity values and the first and second derivatives of the vertical and horizontal directions of different color channels. Support Vector Machines (SVM) were used for classification purposes. An overall recognition rate of 96.5% was achieved using these covariance based features. Relatively low false positive values of 2.4% were obtained which is important to reduce economic loss due to healthy kernels being discarded as fungal damaged. The image processing method is not computationally expensive so that it could be implemented in real-time sorting systems to separate damaged popcorn or other grains that have textural differences.Item Open Access Flame detection method in video using covariance descriptors(IEEE, 2011) Habiboǧlu, Y.H.; Günay, Osman; Çetin, A. EnisVideo fire detection system which uses a spatio-temporal covariance matrix of video data is proposed. This system divides the video into spatio-temporal blocks and computes covariance features extracted from these blocks to detect fire. Feature vectors taking advantage of both the spatial and the temporal characteristics of flame colored regions are classified using an SVM classifier which is trained and tested using video data containing flames and flame colored objects. Experimental results are presented. © 2011 IEEE.Item Open Access Levy walk evolution for global optimization(ACM, 2008-07) Urfalıoğlu, Onay; Çetin, A. Enis; Kuruoğlu, E. E.A novel evolutionary global optimization approach based on adaptive covariance estimation is proposed. The proposed method samples from a multivariate Levy Skew Alpha-Stable distribution with the estimated covariance matrix to realize a random walk and so to generate new solution candidates in the mutation step. The proposed method is compared to the popular Differential Evolution method, which is one of the best general evolutionary global optimizers available. Experimental results indicate that the proposed approach yields a general improvement in the required number of function evaluations to solve global optimization problems. Especially, as shown in experiments, the underlying heavy tailed alpha-stable distribution enables a considerably more effective global search in more complex problems. Track: Evolution Strategies.Item Open Access Maximum likelihood estimation of Gaussian mixture models using particle swarm optimization(IEEE, 2010-08) Arı, Çağlar; Aksoy, SelimWe present solutions to two problems that prevent the effective use of population-based algorithms in clustering problems. The first solution presents a new representation for arbitrary covariance matrices that allows independent updating of individual parameters while retaining the validity of the matrix. The second solution involves an optimization formulation for finding correspondences between different parameter orderings of candidate solutions. The effectiveness of the proposed solutions are demonstrated on a novel clustering algorithm based on particle swarm optimization for the estimation of Gaussian mixture models. © 2010 IEEE.Item Open Access Microscopic image classification using sparsity in a transform domain and Bayesian learning(IEEE, 2011) Suhre, Alexander; Erşahin, Tülin; Çetin-Atalay, Rengül; Çetin, A. EnisSome biomedical images show a large quantity of different junctions and sharp corners. It is possible to classify several types of biomedical images in a region covariance approach. Cancer cell line images are divided into small blocks and covariance matrices of image blocks are computed. Eigen-values of the covariance matrices are used as classification parameters in a Bayesian framework using the sparsity of the parameters in a transform domain. The efficiency of the proposed method over classification using standard Support Vector Machines (SVM) is demonstrated on biomedical image data. © 2011 EURASIP.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 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 Object tracking under illumination variations using 2D-cepstrum characteristics of the target(IEEE, 2010) Cogun, Fuat; Çetin, A. EnisMost video processing applications require object tracking as it is the base operation for real-time implementations such as surveillance, monitoring and video compression. Therefore, accurate tracking of an object under varying scene conditions is crucial for robustness. It is well known that illumination variations on the observed scene and target are an obstacle against robust object tracking causing the tracker lose the target. In this paper, a 2D-cepstrum based approach is proposed to overcome this problem. Cepstral domain features extracted from the target region are introduced into the covariance tracking algorithm and it is experimentally observed that 2D-cepstrum analysis of the target object provides robustness to varying illumination conditions. Another contribution of the paper is the development of the co-difference matrix based object tracking instead of the recently introduced covariance matrix based method. ©2010 IEEE.Item Open Access Target detection and classification in SAR images using region covariance and co-difference(SPIE, 2009-04) Duman, Kaan; Eryıldırım, Abdulkadir; Çetin, A. EnisIn this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed. The new approach is based on region covariance (RC) method which involves the computation of a covariance matrix whose entries are used in target detection and classification. In addition the region co-difference matrix is also introduced. Experimental results of object detection in MSTAR (moving and stationary target recognition) database are presented. The RC and region co-difference method delivers high detection accuracy and low false alarm rates. It is also experimentally observed that these methods produce better results than the commonly used principal component analysis (PCA) method when they are used with different distance metrics introduced. © 2009 SPIE.Item Open Access Unsupervised classification of remotely sensed images using Gaussian mixture models and particle swarm optimization(IEEE, 2010) Arı, Çağlar; Aksoy, SelimGaussian mixture models (GMM) are widely used for un-supervised classification applications in remote sensing. Expectation-Maximization (EM) is the standard algorithm employed to estimate the parameters of these models. However, such iterative optimization methods can easily get trapped into local maxima. Researchers use population-based stochastic search algorithms to obtain better estimates. We present a novel particle swarm optimization-based algorithm for maximum likelihood estimation of Gaussian mixture models. The proposed approach provides solutions for important problems in effective application of population-based algorithms to the clustering problem. We present a new parametrization for arbitrary covariance matrices that allows independent updating of individual parameters during the search process. We also describe an optimization formulation for identifying the correspondence relations between different parameter orderings of candidate solutions. Experiments on a hyperspectral image show better clustering results compared to the commonly used EM algorithm for estimating GMMs. © 2010 IEEE.Item Open Access Video fire detection-Review(Elsevier, 2013) Çetin, A. Enis; Dimitropoulos, K.; Gouverneur, B.; Grammalidis, N.; Günay, O.; Habiboğlu, Y. H.; Töreyin, B. U.; Verstockt, S.This is a review article describing the recent developments in Video based Fire Detection (VFD). Video surveillance cameras and computer vision methods are widely used in many security applications. It is also possible to use security cameras and special purpose infrared surveillance cameras for fire detection. This requires intelligent video processing techniques for detection and analysis of uncontrolled fire behavior. VFD may help reduce the detection time compared to the currently available sensors in both indoors and outdoors because cameras can monitor "volumes" and do not have transport delay that the traditional "point" sensors suffer from. It is possible to cover an area of 100 km2 using a single pan-tilt-zoom camera placed on a hilltop for wildfire detection. Another benefit of the VFD systems is that they can provide crucial information about the size and growth of the fire, direction of smoke propagation.