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Hidden Markov Models With Spectral Features for 2D Shape Recognition [PDF]

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
exaly   +2 more sources

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Spectral Factor Models

Journal of Financial Economics, 2018
We represent risk factors as sums of orthogonal components capturing fluctuations with cycles of different length. The representation leads to novel spectral factor models in which systematic risk is allowed (without being forced) to vary across frequencies. Frequency-specific systematic risk is captured by a notion of spectral beta.
Federico Maria Bandi, Shomesh Chaudhuri, Andrew W. Lo, Andrea Tamoni   +3 more
openaire   +1 more source

Spectral–Spatial Morphological Attention Transformer for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing, 2023
In recent years, convolutional neural networks (CNNs) have drawn significant attention for the classification of hyperspectral images (HSIs). Due to their self-attention mechanism, the vision transformer (ViT) provides promising classification ...
Swalpa Kumar Roy, Ankur Deria, Chiranjibi Shah, J. Haut, Qian Du, A. Plaza   +5 more
semanticscholar   +1 more source

Cluster-based spectral models for a robust assessment of soil properties

Geoderma, 2019
Soil spectroscopy has proven efficient for providing quantitative properties of soil chromophores and soil classification. However, building generic/global models might bring to light some problems which stem from the false premise that each chromophore ...
Yaron Ogen, Jacqueline Zaluda, Nicolas Francos, N. Goldshleger, E. Ben-Dor   +4 more
semanticscholar   +1 more source

SPEECH SPECTRAL SEGMENTATION FOR SPECTRAL ESTIMATION AND FORMANT MODELLING

Speech and Hearing, 2005
The 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, Anthony G. Constantinides   +1 more
openaire   +1 more source

Spectral jitter modeling and estimation

Biomedical Signal Processing and Control, 2009
This 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
openaire   +2 more sources

Generalised Linear Spectral Models

2013
In this chapter we consider 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 generalised autocovariances. Direct Whittle estimation of the
Proietti, Tommaso, LUATI, ALESSANDRA
openaire   +2 more sources

The Spectral Method for General Mixture Models

SIAM Journal on Computing, 2005
We 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, Hadi Salmasian, Santosh S. Vempala   +2 more
openaire   +2 more sources