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» Robust Kernel Principal Component Analysis
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ICIP
2008
IEEE
14 years 9 months ago
Principal Component Analysis of spectral coefficients for mesh watermarking
This paper proposes a new robust 3-D object blind watermarking method using constraints in the spectral domain. Mesh watermarking in spectral domain has the property of spreading ...
Ming Luo, Adrian G. Bors
ACIVS
2008
Springer
14 years 1 months ago
Video-Based Fall Detection in the Home Using Principal Component Analysis
This paper presents the design and real-time implementation of a fall-detection system, aiming at detecting fall incidents in unobserved home situations. The setup employs two fix...
Lykele Hazelhoff, Jungong Han, Peter H. N. de With
PAMI
2008
200views more  PAMI 2008»
13 years 7 months ago
Principal Component Analysis Based on L1-Norm Maximization
In data-analysis problems with a large number of dimension, principal component analysis based on L2-norm (L2PCA) is one of the most popular methods, but L2-PCA is sensitive to out...
Nojun Kwak
ICPR
2000
IEEE
14 years 8 months ago
Sign of Gaussian Curvature from Eigen Plane Using Principal Components Analysis
This paper describes a new method to recover the sign of the local Gaussian curvature at each point on the visible surface of a 3-D object. Multiple (p > 3) shaded images are a...
Shinji Fukui, Yuji Iwahori, Akira Iwata, Robert J....
ACL
2004
13 years 9 months ago
A Kernel PCA Method for Superior Word Sense Disambiguation
We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Principal Component Analysis (KPCA) technique to achieve accuracy superior to the best pu...
Dekai Wu, Weifeng Su, Marine Carpuat