This is a conceptual presentation of video watermarking technique using SVD transform. SVD is sufficient and the most optimal in a given image. It is packed with energy in a given number of transformation coefficients are maximized and is easy to calculate. A method of watermarking embedding and extraction is used in this paper. Investigation of the video using SVD will give good results. The o...

A data set with n measurements on p variables can be represented by an n × p data matrix X. In highdimensional settings where p is large, it is often desirable to work with a low-rank approximation to the data matrix. The most prevalent low-rank approximation is the singular value decomposition (SVD). Given X, an n × p data matrix, the SVD factorizes X as X = UDV ′, where U ∈ Rn×n and V ∈ Rp×p ...

White matter lesion (WML) in magnetic resonance imaging is commonly observed in patients with cerebral small vessel disease (SVD), but the pathological mechanism of WML in SVD is still unclear. We observed the metabolism and microscopic anatomy of white matter in SVD patients. Twelve subjects clinically diagnosed with SVD and 6 normal control subjects were examined with magnetic resonance spect...

We develop a dictionary learning algorithm by minimizing the `1 distortion metric on the data term, which is known to be robust for non-Gaussian noise contamination. The proposed algorithm exploits the idea of iterative minimization of weighted `2 error. We refer to this algorithm as `1-K-SVD, where the dictionary atoms and the corresponding sparse coefficients are simultaneously updated to min...

Singular value decomposition (SVD) is a matrix decomposition algorithm that returns the optimal (in the sense of squared error) low-rank decomposition of a matrix. SVD has found widespread use across a variety of machine learning applications, where its output is interpreted as compact and informative representations of data. The Netflix Prize challenge, and collaborative filtering in general, ...

The paper is concerned with the hidden dynamic state estimation in linear discrete-time stochastic systems in presence of Gaussian noises. The associated with the state-space model estimator is known as the Kalman filter (KF). One of the shortcomings of this recursive algorithm is its numerical instability with respect to roundoff errors. Since the appearance of the KF in 1960s, much effort has...

Subspace-based methods rely on singular value decomposition (SVD) of the sample covariance matrix (SCM) to compute the array signal or noise subspace. For large array, triditional subspace-based algorithms inevitably lead to intensive computational complexity due to both calculating SCM and performing SVD of SCM. To circumvent this problem, a NyströmBased algorithm for array subspace estimation...

With the rapid growth of network multimedia systems and other numerical technologies, images, audio, text and video can be more easily produced, processed as well as stored by digital devices in recent years. To conceal data in transmitting message for copyright protection the secret is very important. Various Digital Watermarking Techniques are developed to protect the secret data. This paper ...

The Singular Value Decomposition (SVD) has many applications in image processing. The SVD can be used to restore a corrupted image by separating significant information from the noise in the image data set. This thesis outlines broad applications that address current problems in digital image processing. In conjunction with SVD filtering, image compression using the SVD is discussed, including ...

background: paraoxonase-1(pon1), a high-density lipoprotein (hdl) associated enzyme, is believed to contribute in the patho­genesis of coronary artery disease (cad). the aim of this study was to evaluate the association of pon1 promoter c (-107)t polymorphism with the extent of coronary artery stenosis in iranian patients. methods : the rflp analysis for determination of the c(-107)t genotype d...