Results 271 to 280 of about 322,666 (307)

Using spectral features for modelbase partitioning

Proceedings of 13th International Conference on Pattern Recognition, 1996
We present an eigenvalue or spectral representation for CAD models to be used in conjunction with the more traditional attributed graph based representation of these models. The eigenvalues provide a gross description of the structure of the objects, and help to divide a large modelbase into structurally homogeneous partitions. Models in each partition
Kuntal Sengupta, Kim L. Boyer
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Salient spectral features for points detection

2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2016
In this paper we introduce a novel method for detecting salient features points on 3D meshes. The contribution of the proposed method is to detect salient feature points in the dual graph spectral domain instead of spatial one. A dual graph Laplacian spectrum of 3D shape is firstly computed for each triangles of the shape. Then we compute the geometric
Nabi Habiba, Ali Douik
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Evaluation of skin spectral features for biometrie

2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP), 2017
In the recent years, multispectral imaging has been successfully used in various biometric authentication applications. However, in most cases, the frames of multispectral images are consolidated simply by using data fusion techniques rather than contributing directly to the recognition process.
Li, Chao, Benezeth, Yannick, Nakamura, Keisuke, Gomez, Randy, Yang, Fan   +4 more
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Channel compensation of modulation spectral features

Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03., 2003
We propose a new channel compensation method for modulation spectral features. We compare our proposed method, subband normalization, with a more traditional method, cepstral mean subtraction (CMS). Experimental results show that subband normalized modulation scale features provide advantages over CMS features. The proposed method is not only robust to
Somsak Sukittanon, Les E. Atlas
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Formant position based weighted spectral features for emotion recognition

Speech Communication, 2011
In this paper, we propose novel spectrally weighted mel-frequency cepstral coefficient (WMFCC) features for emotion recognition from speech. The idea is based on the fact that formant locations carry emotion-related information, and therefore critical ...
Elif Bozkurt, Engin Erzin, A Tanju Erdem   +2 more
exaly   +2 more sources

Bi-Level Spectral Feature Selection

IEEE Transactions on Neural Networks and Learning Systems
Unsupervised feature selection (UFS) aims to learn an indicator matrix relying on some characteristics of the high-dimensional data to identify the features to be selected. However, traditional unsupervised methods perform only at the feature level, i.e., they directly select useful features by feature ranking.
Zebiao Hu, Jian Wang 0010, Kai Zhang 0029, Witold Pedrycz, Nikhil R. Pal   +4 more
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Spectral Features for Synthetic Speech Detection

IEEE Journal of Selected Topics in Signal Processing, 2017
Recent advancements in voice conversion (VC) and speech synthesis research make speech-based biometric systems highly prone to spoofing attacks. This can provoke an increase in false acceptance rate in such systems and requires countermeasure to mitigate such spoofing attacks.
Dipjyoti Paul, Monisankha Pal, Goutam Saha 0001   +2 more
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Spatial and Spectral Features

1997
The goal of pattern analysis, in general, is to transform signals into symbolic descriptions [Dud73, Nie90a, Pen86]. For simple classification problems this corresponds to the computation of a class number for an observed signal (c.f. Figure 6.1). Since the amount of data is too high, if images or speech signals are used directly, the signals are ...
Dietrich W. R. Paulus, Joachim Hornegger
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Face recognition using spectral features

Pattern Recognition, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fei Wang 0001, Jingdong Wang 0001, Changshui Zhang, James T. Kwok   +3 more
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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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