Reconstruction and Recognition of Tensor-Based Objects With Concurrent Subspaces Analysis

Principal components analysis (PCA) has traditionally been utilized with data expressed in the form of 1-D vectors, but there exists much data such as gray-level images, video sequences, Gabor-filtered images and so on, that are intrinsically in the form of second or higher order tensors. For repres...

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Vydané v:	IEEE transactions on circuits and systems for video technology Ročník 18; číslo 1; s. 36 - 47
Hlavní autori:	Dong Xu, Shuicheng Yan, Lei Zhang, Lin, S., Hong-Jiang Zhang, Huang, T.S.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.01.2008 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Applied sciences Asia Concurrent subspaces analysis (CSA) Detection, estimation, filtering, equalization, prediction dimensionality reduction Exact sciences and technology Face recognition Image analysis Image processing Image reconstruction Image sequence analysis Information, signal and communications theory Mathematical analysis Matrix decomposition Object recognition object reconstruction object representation Pattern recognition Principal component analysis Principal components analysis principal components analysis (PCA) Projection Reconstruction Signal and communications theory Signal processing Signal, noise Studies Subspaces Telecommunications and information theory Tensile stress Tensors Video sequences Object Reconstruction Dimensionality Reduction Concurrent Subspaces Analysis Object Representation Principal Components Analysis Performance evaluation Gabor filter Second order Concurrent subspaces analysis (CSA) Grey level image Image processing Third order Video signal Subspace method Iterative method Pattern recognition Object recognition Algorithm Image reconstruction Statistical method object reconstruction dimensionality reduction Image sequence object representation Object detection Object oriented principal components analysis (PCA) Target detection Principal component analysis
ISSN:	1051-8215, 1558-2205
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