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» Principal Component Analysis Based on L1-Norm Maximization
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ICIP
2006
IEEE
14 years 9 months ago
Fusion of Visible and Infrared Images using Empirical Mode Decomposition to Improve Face Recognition
In this effort, we propose a new image fusion technique, utilizing Empirical Mode Decomposition (EMD), for improved face recognition. EMD is a non-parametric datadriven analysis t...
Harishwaran Hariharan, Andreas Koschan, Besma R. A...
VIS
2008
IEEE
214views Visualization» more  VIS 2008»
14 years 8 months ago
Estimating Crossing Fibers: A Tensor Decomposition Approach
Diffusion weighted magnetic resonance imaging is a unique tool for non-invasive investigation of major nerve fiber tracts. Since the popular diffusion tensor (DT-MRI) model is limi...
Thomas Schultz, Hans-Peter Seidel
VIS
2003
IEEE
121views Visualization» more  VIS 2003»
14 years 8 months ago
Hierarchical Clustering for Unstructured Volumetric Scalar Fields
We present a method to represent unstructured scalar fields at multiple levels of detail. Using a parallelizable classification algorithm to build a cluster hierarchy, we generate...
Christopher S. Co, Bjørn Heckel, Hans Hagen...
MICCAI
2004
Springer
14 years 8 months ago
Learning Coupled Prior Shape and Appearance Models for Segmentation
We present a novel framework for learning a joint shape and appearance model from a large set of un-labelled training examples in arbitrary positions and orientations. The shape an...
Xiaolei Huang, Zhiguo Li, Dimitris N. Metaxas
ICML
2008
IEEE
14 years 8 months ago
Expectation-maximization for sparse and non-negative PCA
We study the problem of finding the dominant eigenvector of the sample covariance matrix, under additional constraints on the vector: a cardinality constraint limits the number of...
Christian D. Sigg, Joachim M. Buhmann