Automatic motion capture data denoising via filtered subspace clustering and low rank matrix approximation

In this paper, we present an automatic Motion Capture (MoCap) data denoising approach via filtered subspace clustering and low rank matrix approximation. Within the proposed approach, we formulate the MoCap data denoising problem as a concatenation of piecewise motion matrix recovery problem. To thi...

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Bibliographic Details
Published in:	Signal processing Vol. 105; pp. 350 - 362
Main Authors:	Liu, Xin, Cheung, Yiu-ming, Peng, Shu-Juan, Cui, Zhen, Zhong, Bineng, Du, Ji-Xiang
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.12.2014 Elsevier
Subjects:	Accelerated proximal gradient Applied sciences Detection, estimation, filtering, equalization, prediction Exact sciences and technology Filtered subspace clustering Image processing Information, signal and communications theory Low-rank matrix approximation MoCap data denoising Moving average filter Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Telecommunications and information theory Filtered subspace clustering Moving average filter MoCap data denoising Accelerated proximal gradient Low-rank matrix approximation Performance evaluation Automatic classification State of the art Moving average Noise reduction Subspace method Signal classification Learning Concatenation Signal processing Motion detection Gradient method
ISSN:	0165-1684, 1872-7557
Online Access:	Get full text
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