Estimating the number of segments in time series data using permutation tests
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Signal segmentation and denoising algorithm based on energy optimisation
Signal Processing
Any dimension polygonal approximation based on equal errors principle
Pattern Recognition Letters
Equivalent key frames selection based on iso-content principles
IEEE Transactions on Circuits and Systems for Video Technology
Shape-Motion based athlete tracking for multilevel action recognition
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
IEEE Transactions on Signal Processing
Sampling Moments and Reconstructing Signals of Finite Rate of Innovation: Shannon Meets Strang–Fix
IEEE Transactions on Signal Processing
Exact Bayesian curve fitting and signal segmentation
IEEE Transactions on Signal Processing
A speech/music discriminator based on RMS and zero-crossings
IEEE Transactions on Multimedia
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In this paper, we propose a method for time interval segmentation of signals based on an EquiPartition principle (EP). According to EP, the signal is segmented into segments that give equal errors in reconstruction selecting the most suitable model to describe each segment. Moreover, the segments are equivalent in the content domain, since the signal is segmented into segments that are modelled by the same number of coefficients. The proposed method has been successfully applied on different types of signals like: physiologic, speech, human motion, financial time series. Finally, the proposed methodology is very flexible on changes of error criteria, signal modelling and on signal dimension yielding a robust method for segmentation and modelling of signals.