摘要
复小波克服了单小波的缺点,具有时移不变性、方向性信息多和相位信息等特点·从能量角度出发,主要研究了不同复小波变换方法的一阶和二阶统计矩(共生矩阵)特性,并应用于纹理特征的提取,与传统的单小波做了比较·通过理论分析和在纹理图像检索的对比实验数据说明了复小波在纹理特征提取方面的性能优于单小波,采用一阶和二阶统计矩相结合方法的性能最好,检索精度提高了8%·
Complex wavelet transform overcomes the drawbacks of discrete wavelet transform, such as shift sensitivity, poor directionality and lack of the phase information. In this paper, the performance of the first-order and the second-order （co-occurrence） statistical characters of the different complex wavelet transforms （CWT） is studied with the consideration of the wavelet energy, and applied to texture feature extraction. It is concluded that the performance of the CWT is better than the pyramid discrete wavelet decomposition transforms （PDWT） on the texture feature extraction through theory analysis and the contrast experiments results on the texture retrieval. Best performance is achieved by combining the first- order signatures with the second-order signatures and the performance of retrieval is raised 8 %.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2005年第10期1746-1751,共6页
Journal of Computer Research and Development
基金
国家自然科学基金项目(60473034)
甘肃省自然科学基金项目(3ZS051-A25-047)
关键词
小波
复小波变换
纹理
纹理特征提取
纹理检索
wavelet
complex wavelet transforms
texture
texture feature extraction
texture retrieval
