This communication describes some image-compression concepts such as the transmitted energy of the digital signal and the relative error of the compressed image in function of the number and magnitude of the singular values used in the Singular Value Decomposition of the matrix that represents the original picture. Comparisons are made with wavelet-based techniques using a test image. Key-Words...

The release of image processing techniques make image modification and fakery easier. Image fakery, here, is defined as a process to copy a region of source image and paste it onto the destination image, with some post processing methods applied, such as boundary smoothing, blurring, etc. to make it natural. The most important characteristic of image fakery is object copy and paste. In order to...

This article presents a new subspace-based technique for reducing the noise of signals in time-series. In the proposed approach, the signal is initially represented as a data matrix. Then using Singular Value Decomposition (SVD), noisy data matrix is divided into signal subspace and noise subspace. In this subspace division, each derivative of the singular values with respect to rank order is u...

Given a complex matrix H, we consider the decomposition H = QRP∗, where R is upper triangular and Q and P have orthonormal columns. Special instances of this decomposition include the singular value decomposition (SVD) and the Schur decomposition where R is an upper triangular matrix with the eigenvalues of H on the diagonal. We show that any diagonal for R can be achieved that satisfies Weyl’s...

A novel saliency detection method for maritime search and rescue using singular value decomposition to the amplitude spectrum is proposed. Color and intensity features of images are extracted and amplitude spectrums of them are obtained by Fourier transform. Singular value decomposition (SVD) is conducted on the amplitude spectrums and the saliency maps are defined as the inverse Fourier transf...

In this paper we derive a new algorithm for constructing a uni-tary decomposition of a sequence of matrices in product or quotient form. The unitary decomposition requires only unitary left and right transformations on the individual matrices and amounts to computing the generalized singular value decomposition of the sequence. The proposed algorithm is related to the classical Golub-Kahan proc...

a r t i c l e i n f o a b s t r a c t Taking into account two types of tremor, namely essential tremor (ET) and Parkinson's disease (PD), which are often misdiagnosed in clinical practice, a novel approach using singular value decomposition (SVD) to extract the features of intrinsic mode functions (IMFs) and support vector machine (SVM) is proposed to distinguish between them. The hand accelera...

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...

In this paper, we present an algorithm for the singular value decomposition (SVD) of a bidiagonal matrix by means of the eigenpairs of an associated symmetric tridiagonal matrix. The algorithm is particularly suited for the computation of a subset of singular values and corresponding vectors. We focus on a sequential implementation, discuss special cases and other issues. We use a large set of ...

In this paper, we propose a novel audio steganography scheme for embedding high-capacity covert data in a music carrier, where the carrier is first transformed to a 2D arrangement (image) and represented by a wavelet domain singular value decomposition (SVD), and a quantization-index-modulation process is then applied on the SVD for the covert data embedding. The proposed scheme, due to its ind...