Browsing by Subject "Covariance matrices"

Now showing 1 - 6 of 6

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.
Open Access
Maximum likelihood estimation of Gaussian mixture models using particle swarm optimization
(IEEE, 2010-08) Arı, Çağlar; Aksoy, Selim
We 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.
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. Enis
Some 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.
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, Rengül
In 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.
Open Access
Secrecy rate and harvested energy trade-off for MISO channels with finite-alphabet inputs
(IEEE, 2018-05) Aghdam, Sina Rezaei; Duman, Tolga M.
We focus on transmit signal design for multiple- input single-output (MISO) wiretap channels with simultaneous wireless information and power transfer (SWIPT). Assuming that the channel inputs are drawn from standard constellation sets, we formulate secrecy rate maximization problems subject to power and harvested energy constraints. We tackle these problems under two different assumptions on the channel state information (CSI) at the transmitter. First, we consider a scenario in which the transmitter knows the CSI for both the information receiver and the energy receiver (potential eavesdropper), and we propose a precoder optimization approach. Then, we investigate the case where only perfect CSI of the information receiver is available along with the statistical CSI of the energy receiver. Our numerical results demonstrate the efficacy of the proposed solutions.
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.