Optimized curvelet-based empirical mode decomposition

Renjie Wu, Qieshi Zhang, Sei Ichiro Kamata

研究成果: Conference contribution


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.

ホスト出版物のタイトルSeventh International Conference on Machine Vision, ICMV 2014
編集者Branislav Vuksanovic, Jianhong Zhou, Antanas Verikas, Petia Radeva
出版ステータスPublished - 2015
イベント7th International Conference on Machine Vision, ICMV 2014 - Milan, Italy
継続期間: 2014 11月 192014 11月 21


名前Proceedings of SPIE - The International Society for Optical Engineering


Conference7th International Conference on Machine Vision, ICMV 2014

ASJC Scopus subject areas

  • 電子材料、光学材料、および磁性材料
  • 凝縮系物理学
  • コンピュータ サイエンスの応用
  • 応用数学
  • 電子工学および電気工学


「Optimized curvelet-based empirical mode decomposition」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。