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Hidden Markov models with spectral features for 2D shape recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001 We present a technique using Markov models with spectral features for recognizing 2D shapes. We analyze the properties of Fourier spectral features derived from closed contours of 2D shapes and use these features for 2D pattern recognition.
Jinhai Cai
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SPEECH SPECTRAL SEGMENTATION FOR SPECTRAL ESTIMATION AND FORMANT MODELLING
Speech and Hearing, 2005The evaluation of accurate speech spectral estimates is of importance in many areas such as formant extraction, speaker/speech recognition etc. This work describes an approach based on Dynamic Progamming for the optimal segmentation of speech spectra into Selective Linear Predictive (LP) segments to minimise the discrepancy between real and model ...
Harprit S. Chhatwal +1 more
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Generalised Linear Spectral Models
SSRN Electronic Journal, 2013AbstractThis chapter considers a class of parametric spectrum estimators based on a generalized linear model for exponential random variables with power link. The power transformation of the spectrum of a stationary process can be expanded in a Fourier series, with the coefficients representing generalized autocovariances.
Proietti, Tommaso, LUATI, ALESSANDRA
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Spectral jitter modeling and estimation
Biomedical Signal Processing and Control, 2009This paper suggests a new method for short-time jitter estimation based on a mathematical model that describes the coupling of two periodical phenomena. Specifically, jitter is modeled as the movement of one of the two periodic phenomena with respect to the other. The proposed method measures this movement indirectly by taking into account the spectral
Miltiadis Vasilakis, Yannis Stylianou
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The Spectral Method for General Mixture Models
SIAM Journal on Computing, 2005We present an algorithm for learning a mixture of distributions based on spectral projection. We prove a general property of spectral projection for arbitrary mixtures and show that the resulting algorithm is efficient when the components of the mixture are logconcave distributions in $\Re^{n}$ whose means are separated.
Ravindran Kannan +2 more
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Spectral Impulse Noise Model for Spectral Image Processing
2015The performance of an image processing algorithm can be assessed through its resulting images. However, in order to do so, both ground truth image and noisy target image with known properties are typically required. In the context of hyperspectral image processing, another constraint is introduced, i.e.
Hilda Deborah +2 more
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