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» Extracting Propositions from Trained Neural Networks
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DAGM
1997
Springer
15 years 1 months ago
A Feature Map Approach to Real-Time 3-D Object Pose Estimation from Single 2-D Perspective Views
A novel approach to the computation of an approximate estimate of spatial object pose from camera images is proposed. The method is based on a neural network that generates pose hy...
S. Winkler, Patrick Wunsch, Gerd Hirzinger
CVPR
1999
IEEE
15 years 11 months ago
Shape from Recognition and Learning: Recovery of 3-D Face Shapes
In this paper, a novel framework for the recovery of 3D surfaces of faces from single images is developed. The underlying principle is shape from recognition, i.e. the idea that p...
Dibyendu Nandy, Jezekiel Ben-Arie
IJCNN
2008
IEEE
15 years 4 months ago
Multifractal feature vectors for Brain-Computer interfaces
—This article introduces a new feature vector extraction for EEG signals using multifractal analysis. The validity of the approach is asserted on real data sets from the BCI comp...
Nicolas Brodu
ISNN
2004
Springer
15 years 3 months ago
Progressive Principal Component Analysis
Abstract. Principal Component Analysis (PCA) is a feature extraction approach directly based on a whole vector pattern and acquires a set of projections that can realize the best r...
Jun Liu, Songcan Chen, Zhi-Hua Zhou
ICASSP
2011
IEEE
14 years 1 months ago
Sparse common spatial patterns in brain computer interface applications
The Common Spatial Pattern (CSP) method is a powerful technique for feature extraction from multichannel neural activity and widely used in brain computer interface (BCI) applicat...
Fikri Goksu, Nuri Firat Ince, Ahmed H. Tewfik