Signal segmentation and denoising algorithm based on energy optimisation

Authors:
Sasan Mahmoodi;Bayan S. Sharif
Affiliations:
Psychology Department, School of Biology, Newcastle University, Newcastle upon Tyne, UK;School of Electrical, Electronic and Computer Engineering, Merz Court, Newcastle University, Newcastle upon Tyne, UK
Venue:
Signal Processing
Year:
2005

Citing 7
Cited 4

Nonlinear total variation based noise removal algorithms

Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Variational methods in image segmentation

Variational methods in image segmentation
Image segmentation by variational methods: Mumford and Shah functional and the discrete approximations

SIAM Journal on Applied Mathematics
Tube Methods for BV Regularization

Journal of Mathematical Imaging and Vision
An Inexact Newton-CG-Type Active Contour Approach for the Minimization of the Mumford-Shah Functional

Journal of Mathematical Imaging and Vision
Active contours without edges

IEEE Transactions on Image Processing
Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification

IEEE Transactions on Image Processing

A nonlinear variational method for signal segmentation and reconstruction using level set algorithm

Signal Processing - Special section: Distributed source coding
Signal segmentation and modelling based on equipartition principle

DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Nonlinear optimisation method for image segmentation and noise reduction using geometrical intrinsic properties

Image and Vision Computing
Automatic segmentation of long-term ECG signals corrupted with broadband noise based on sample entropy

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.09

Visualization

Abstract

A nonlinear functional is considered in this short communication for time interval segmentation and noise reduction of signals. An efficient algorithm that exploits the signal geometrical properties is proposed to optimise the nonlinear functional for signal smoothing. Discontinuities separating consecutive time intervals of the original signal are initially detected by measuring the curvature and arc length of the smoothed signal. The nonlinear functional is then optimised for each time interval to achieve noise reduction of the original noisy signal. This algorithm exhibits robustness for signals characterised by very low signal to noise ratios.