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» Extracting general-purpose features from LIDAR data
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GRAPHITE
2003
ACM
14 years 2 months ago
Smooth surface reconstruction from noisy range data
This paper shows that scattered range data can be smoothed at low cost by fitting a Radial Basis Function (RBF) to the data and convolving with a smoothing kernel (low pass filt...
Jonathan C. Carr, Richard K. Beatson, Bruce C. McC...
ICMCS
2005
IEEE
145views Multimedia» more  ICMCS 2005»
14 years 3 months ago
From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification
Little attention has been paid so far to physiological signals for emotion recognition compared to audio-visual emotion channels, such as facial expressions or speech. In this pap...
Johannes Wagner, Jonghwa Kim, Elisabeth Andr&eacut...
ICASSP
2010
IEEE
13 years 8 months ago
Speech/Non-Speech Detection in Meetings from Automatically Extracted low Resolution Visual Features
In this paper we address the problem of estimating who is speaking from automatically extracted low resolution visual cues in group meetings. Traditionally, the task of speech/non...
Hayley Hung, Sileye O. Ba
ICA
2012
Springer
12 years 5 months ago
Multi-domain Feature of Event-Related Potential Extracted by Nonnegative Tensor Factorization: 5 vs 14 Electrodes EEG Data
As nonnegative tensor factorization (NTF) is particularly useful for the problem of underdetermined linear transform model, we performed NTF on the EEG data recorded from 14 electr...
Fengyu Cong, Anh Huy Phan, Piia Astikainen, Qibin ...
KDD
1995
ACM
129views Data Mining» more  KDD 1995»
14 years 1 months ago
Feature Extraction for Massive Data Mining
Techniques for learning from data typically require data to be in standard form. Measurements must be encoded in a numerical format such as binary true-or-false features, numerica...
V. Seshadri, Raguram Sasisekharan, Sholom M. Weiss