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» Subspace Analysis Using Random Mixture Models
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MM
2004
ACM
248views Multimedia» more  MM 2004»
14 years 1 months ago
Incremental semi-supervised subspace learning for image retrieval
Subspace learning techniques are widespread in pattern recognition research. They include Principal Component Analysis (PCA), Locality Preserving Projection (LPP), etc. These tech...
Xiaofei He

Publication
1851views
15 years 8 months ago
Cerebrovascular Segmentation from TOF Using Stochastic Models
In this paper, we present an automatic statistical approach for extracting 3D blood vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) data. The voxels of the d...
M. Sabry Hassouna, Aly A. Farag, Stephen Hushek, T...
MICCAI
2007
Springer
14 years 8 months ago
Hyperspherical von Mises-Fisher Mixture (HvMF) Modelling of High Angular Resolution Diffusion MRI
A mapping of unit vectors onto a 5D hypersphere is used to model and partition ODFs from HARDI data. This mapping has a number of useful and interesting properties and we make a li...
Abhir Bhalerao, Carl-Fredrik Westin
KDD
2001
ACM
187views Data Mining» more  KDD 2001»
14 years 8 months ago
Random projection in dimensionality reduction: applications to image and text data
Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the method preserves distances quite nicely; however,...
Ella Bingham, Heikki Mannila
FGR
2008
IEEE
346views Biometrics» more  FGR 2008»
14 years 2 months ago
Markov random field models for hair and face segmentation
This paper presents an algorithm for measuring hair and face appearance in 2D images. Our approach starts by using learned mixture models of color and location information to sugg...
Kuang-chih Lee, Dragomir Anguelov, Baris Sumengen,...