Texture classification through directional empirical mode decomposition

CASIA OpenIR > 智能制造技术与系统研究中心 > 多维数据分析（彭思龙）-技术团队

	Texture classification through directional empirical mode decomposition
	Liu Zhongxuan; Wang Hongjian; Peng Silong; Zhongxuan Liu
	2004
会议名称	Proceedings of the 17th International Conference on Pattern Recognition ICPR 2004
页码	pp 803-806
会议日期	2004/8/23-2004/8/26
会议地点	United kingdomCambridgeUnitedkingdom
摘要	This paper presents a method for texture classification through Directional Empirical Mode Decomposition (DEMD). Although there have been many filtering based techniques proposed for texture retrieval problems of non-adaptivity and redundancy are still hard to solve simultaneously. As a technique being introduced into signal processing recently Empirical Mode Decomposition (EMD) is an adaptive and approximately orthogonal filtering process. To apply EMD to texture classification we propose a new method of extending 1-D EMD to 2-D case called DEMD. The approach adaptively decomposes images into local narrow band ingredients-Intrinsic Mode Functions (IMFs) and extracts their features including frequency and envelopes. To improve its classification ability the fractal dimensions of the IMFs are also considered. Decomposition of several directions is computed for rotation invariance. Experiments for textures in Brodatz set and USC database indicate the effectiveness of our technique.
关键词	无
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12899
专题	智能制造技术与系统研究中心_多维数据分析（彭思龙）-技术团队
通讯作者	Zhongxuan Liu
推荐引用方式 GB/T 7714	Liu Zhongxuan,Wang Hongjian,Peng Silong,et al. Texture classification through directional empirical mode decomposition[C],2004:pp 803-806.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Texture classificati（504KB）	会议论文		暂不开放	CC BY-NC-SA