Results 261 to 270 of about 1,051,339 (291)

Feature Extraction by Linear Spectral Unmixing

2004
Linear Spectral Unmixing (LSU) has been proposed for the analysis of hyperspectral images, to compute the fractional contribution of the detected endmembers to each pixel in the image. In this paper we propose that the fractional abundance coefficients to be used as features for the supervised classification of the pixels. Thus we compare them with two
Manuel Graña, Alicia D'Anjou
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Atlas of M7III Spectral Features in the Optical

Astrophysics and Space Science, 2003
The difficulties encountered in attempts to study the spectra of very late stars inspired us to prepare this spectral atlas containing essentially spectral features of M7III stars in the optical spectral region. As representative star we took the cool component of the binary symbiotic CH Cygni during one of its quiescent phases.
Kotnik-Karuza, Dubravka, Jurdana-Šepić, Rajka   +1 more
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Spatial and spectral features of polariton fluorescence

Journal of Luminescence, 1979
The spatial properties of polariton fluorescence are shown to be of crucial importance to determine polariton fluorescence lineshapes. Comparison in GaAs of front and back-side fluorescence under resonant and non-resonant optical excitation of the no-phonon line and its LO-replica gives evidence of this spatial dependence.
C. Weisbuch, R. G. Ulbrich
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Spectral representations of linear features for generalisation

1995
In this paper we propose the use of new representations of linear features in order to make up for the weakness of classical generalisation algorithms. Such representations are developed from spectral tools: Fourier series and wavelet decomposition.
Emmanuel Fritsch, Jean Philippe Lagrange
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Object recognition by clustering spectral features

Proceedings. International Conference on Image Processing, 2003
We investigate whether vectors of graph spectral features can be used for the purposes of graph clustering. We commence from the eigenvalues and eigenvectors of the adjacency matrix. Each of the leading eigenmodes represents a cluster of nodes and is mapped to a component of a feature vector.
Bin Luo 0001, Richard C. Wilson 0001, Edwin R. Hancock   +2 more
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Features of the Spectral Density of a Spin System

Doklady Mathematics, 2018
The article deals with thermodynamic properties of systems with classical spins. In particular, in the limit of large number \(N\) of spins the author finds a connection between the free energy \(f(\beta )\) (at temperature \(T=1/k \beta\)) and the `spectral density' \(D(E)\), i.e., the degeneracy of a generic energy level \(E\).
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Spectral transition features in dysarthric speech.

2008
Some spectral transition features are introduced and tested in samples from dysarthric patients. The goal is to explore their potential as descriptors of articulatory deviations. This preliminary analysis includes only stop consonants extracted from the diadochokinetic task.
Hernández–díaz Huici, Maria E., Verhelst, Werner   +1 more
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Shape recognition using spectral features

Pattern Recognition Letters, 1998
Summary: The classification of planar shapes using spectral features is presented in this paper. The contour of a planar shape is represented by the magnitude and phase of radial vectors drawn from a centroid, and they are modeled by an autoregressive process. The spectral features are extracted from the least squares estimators of the model parameters,
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Feature Selection for Enhanced Spectral Shape Comparison

Eurographics Workshop on 3D Object Retrieval, EG 3DOR, 2010
n the context of shape matching, this paper proposes a framework for selecting the Laplacian eigenvalues of 3D shapes that are more relevant for shape comparison and classification. Three approaches are compared to identify a specific set of eigenvalues such that they maximise the retrieval and/or the classification performance on the input benchmark ...
S Marini, G Patanè, M Spagnuolo, B Falcidieno   +3 more
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A Multiscale Spectral Features Graph Fusion Method for Hyperspectral Band Selection

IEEE Transactions on Geoscience and Remote Sensing, 2022
Wei-Wei Sun, Gang Yang, Jiangtao Peng
exaly