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Proceedings Paper

Optimized curvelet-based empirical mode decomposition
Author(s): Renjie Wu; Qieshi Zhang; Sei-ichiro Kamata
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Paper Abstract

The recent years has seen immense improvement in the development of signal processing based on Curvelet transform. The Curvelet transform provide a new multi-resolution representation. The frame elements of Curvelets exhibit higher direction sensitivity and anisotropic than the Wavelets, multi-Wavelets, steerable pyramids, and so on. These features are based on the anisotropic notion of scaling. In practical instances, time series signals processing problem is often encountered. To solve this problem, the time-frequency analysis based methods are studied. However, the time-frequency analysis cannot always be trusted. Many of the new methods were proposed. The Empirical Mode Decomposition (EMD) is one of them, and widely used. The EMD aims to decompose into their building blocks functions that are the superposition of a reasonably small number of components, well separated in the time-frequency plane. And each component can be viewed as locally approximately harmonic. However, it cannot solve the problem of directionality of high-dimensional. A reallocated method of Curvelet transform (optimized Curvelet-based EMD) is proposed in this paper. We introduce a definition for a class of functions that can be viewed as a superposition of a reasonably small number of approximately harmonic components by optimized Curvelet family. We analyze this algorithm and demonstrate its results on data. The experimental results prove the effectiveness of our method.

Paper Details

Date Published: 14 February 2015
PDF: 5 pages
Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 94451O (14 February 2015); doi: 10.1117/12.2180847
Show Author Affiliations
Renjie Wu, Waseda Univ. (Japan)
Qieshi Zhang, Waseda Univ. (Japan)
Sei-ichiro Kamata, Waseda Univ. (Japan)

Published in SPIE Proceedings Vol. 9445:
Seventh International Conference on Machine Vision (ICMV 2014)
Antanas Verikas; Branislav Vuksanovic; Petia Radeva; Jianhong Zhou, Editor(s)

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