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CVPR
2008
IEEE
16 years 2 months ago
Parameterized Kernel Principal Component Analysis: Theory and applications to supervised and unsupervised image alignment
Parameterized Appearance Models (PAMs) (e.g. eigentracking, active appearance models, morphable models) use Principal Component Analysis (PCA) to model the shape and appearance of...
Fernando De la Torre, Minh Hoai Nguyen
100
Voted
DATASCIENCE
2002
84views more  DATASCIENCE 2002»
15 years 5 days ago
The application of Principal Component Analysis to materials science data
The relationship between apparently disparate sets of data is a critical component of interpreting materials' behavior, especially in terms of assessing the impact of the mic...
Changwon Suh, Arun Rajagopalan, Xiang Li, Krishna ...
112
Voted
COMPLIFE
2006
Springer
15 years 4 months ago
Set-Oriented Dimension Reduction: Localizing Principal Component Analysis Via Hidden Markov Models
We present a method for simultaneous dimension reduction and metastability analysis of high dimensional time series. The approach is based on the combination of hidden Markov model...
Illia Horenko, Johannes Schmidt-Ehrenberg, Christo...
110
Voted
ICIP
2008
IEEE
16 years 2 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
15 years 6 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