摘要
纹理分割已经取得了很大的进展 ,但仍然缺乏一个轻便的解决方案 建立了一个无监督纹理分割框架 ,其核心是将Gabor滤波器所提取的特征视为统计量 ,用独立分量分析 (ICA)整合特征 ,并用独立分量作为新的纹理特征 ,避开了Gabor滤波器参数选择的难题 实验结果表明 ,ICA比主分量分析更利于纹理特征重整
Despite the great progress of unsupervised texture segmentation, it still lacks a portable and unified solution to address the diversity of textures A new framework to gear to the need is presented to take texture features extracted directly by Gabor filters as statistic values The distinct points are: (1) the framework avoids the difficult problem of selecting parameters of Gabor filters; (2) it integrates texture features using independent component analysis(ICA); and (3)it treats the independent components as new texture features Experiments on mosaic natural texture images show the proposed framework gives satisfactory results compared to those based on principal component analysis
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2004年第3期284-289,296,共7页
Journal of Computer-Aided Design & Computer Graphics
基金
国家"八六三"高技术研究发展计划 ( 2 0 0 1AA2 3 10 3 1)
国家重点基础研究发展规划项目 (G19980 3 0 60 8)
国家科技攻关计划课题奥运科技专项 ( 2 0 0 1BA90 4B0 8)
中国科学院计算技术研究所青年创新基金( 2 0 0 2 6180 4)资助
关键词
图像理解
计算机视觉
纹理分割
GABOR滤波器
ICA
特征提取
特征选择
unsupervised texture segmentation
Gabor filters
independent component analysis
principal component analysis
feature integration
