Browsing by Keywords "Covariance matrix"
Now showing items 1-14 of 14
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Average error in recovery of sparse signals and discrete fourier transform
(IEEE, 2012-04)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 ... -
Counting surrounding nodes using DS-SS signals and de Bruijn sequences in blind environment
(IEEE, 2013-03)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 ... -
Covariance matrix-based fire and flame detection method in video
(Springer, 2011-09-17)This 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 ... -
Detection of fungal damaged popcorn using image property covariance features
(Elsevier, 2012)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 ... -
Flame detection method in video using covariance descriptors
(IEEE, 2011)Video 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 ... -
Levy walk evolution for global optimization
(ACM, 2008-07)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 ... -
Maximum likelihood estimation of Gaussian mixture models using particle swarm optimization
(IEEE, 2010-08)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 ... -
Microscopic image classification using sparsity in a transform domain and Bayesian learning
(IEEE, 2011)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 ... -
Microscopic image classification via WT-based covariance descriptors using Kullback-Leibler distance
(IEEE, 2012)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 ... -
A multiplication-free framework for signal processing and applications in biomedical image analysis
(IEEE, 2013)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 ... -
Object tracking under illumination variations using 2D-cepstrum characteristics of the target
(IEEE, 2010)Most 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 ... -
Target detection and classification in SAR images using region covariance and co-difference
(SPIE, 2009-04)In 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 ... -
Unsupervised classification of remotely sensed images using Gaussian mixture models and particle swarm optimization
(IEEE, 2010)Gaussian 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. ... -
Video fire detection-Review
(Elsevier, 2013)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 ...
