A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
Analog and digital communication systems (4th ed.)
Analog and digital communication systems (4th ed.)
A fresh look at the Hough transform
Pattern Recognition Letters
Image Processing - Principles and Applications
Image Processing - Principles and Applications
Newborn EEG seizure pattern characterisation using time-frequency analysis
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
Performance of quadratic time-frequency distributions as instantaneous frequency estimators
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Signal enhancement by time-frequency peak filtering
IEEE Transactions on Signal Processing
Analysis of multicomponent LFM signals by a combined Wigner-Houghtransform
IEEE Transactions on Signal Processing
Morphological image processing for FM source detection and localization
Signal Processing
Adaptive windowed cross Wigner-Ville distribution as an optimum phase estimator for PSK signals
Digital Signal Processing
Statistical modeling and denoising Wigner-Ville distribution
Digital Signal Processing
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This paper presents a method for estimating the instantaneous frequency (IF) of multicomponent signals. The technique involves, firstly, the transformation of the one-dimensional signal to the two-dimensional time-frequency (TF) domain using a reduced interference quadratic TF distribution. IF estimation of signal components is then achieved by implementing two image processing steps: local peak detection of the TF representation followed by an image processing technique called component linking. The proposed IF estimator is tested on noisy synthetic monocomponent and multicomponent signals exhibiting linear and nonlinear laws. For low signal-to-noise ratio (SNR) environments, a TF peak filtering preprocessing step is used for signal enhancement. Application of the IF estimation scheme to real signals is illustrated with newborn EEG signals. Finally, to illustrate the potential use of the proposed IF estimation method in classifying signals based on their TF components' IFs, a classification method using least squares data-fitting is proposed and illustrated on synthetic and real signals.