Showing papers by "Ramdas Kumaresan published in 1992"

Fractal dimension in the analysis of medical images

Abstract: The analysis of cardiac magnetic resonance (MR) images and X-rays of bone is considered. Each image type is approached using a different form of fractal parameterization. For the MR images, the goal of the study is segmentation, and to this end small regions of the image are assigned a local value of fractal dimension. For the bone X-rays, rather than segmentation, the large-scale structure is parameterized by its fractal dimension. In both cases, the use of fractals leads to the classification of the parameters of interest. When applied to segmentation, this analysis yields boundary discrimination unavailable through previous methods. For the X-rays, texture changes are quantified and correlated with physical changes in the subject. In both cases, the parameterizations are robust with regard to noise present in the images, as well as to variable contrast and brightness. >

Instantaneous non-linear operators for tracking multicomponent signal parameters

Abstract: The authors have extended the Kaiser-Teager algorithm for separating the contributions of the amplitude modulation and frequency modulation of a single sinusoid to a signal consisting of multiple components. An instantaneous nonlinear operator turns out to be the determinant of a Toeplitz matrix formed with the signal samples. Because of its instantaneously adaptive nature, this algorithm can be used to track parameter variations in the signal components, provided these variations are not too rapid. This is demonstrated using a synthetic signal containing two AM-FM components and a speech signal. The method's relationship to Prony's method is pointed out. >

On accurately tracking the harmonic components' parameters in voiced-speech segments and subsequent modeling by a transfer function

Abstract: An improved method for modeling voiced speech signals is proposed. First, a method to accurately model the signals using a linear combination of harmonically related sinewaves is described. The method fits a linear combination of sines and cosines whose frequencies are integer multiples of the unknown fundamental (pitch) frequency to the speech data in the least-square sense. The amplitudes of the sinewaves and the fundamental frequency are the unknowns and are determined simultaneously using the least-squares fit. It is shown how one can obtain smoothly varying frequency and amplitude tracks for all the harmonics and thus model the speech signal parsimoniously. >

A low rank weighted matrix approximation method for robust estimation of sinusoid parameters

Abstract: Techniques based on linear prediction (LP) and the singular value decomposition (SVD) for the robust estimation of the parameters of closely spaced exponentially damped sinusoidal signals in additive noise are extended and improved. An iterative method of fitting lower-rank least squares approximations subject to a general choice of weights is used. The method is applied to data sequences consisting of one and two signals with impulsive noise or with missing data samples. >