摘要
为了识别死节、活节、虫眼三种木材表面缺陷,采用Gabor变换和模糊C均值聚类进行缺陷分割;采用数学形态学运算对分割图像进行了后处理;获取了木材缺陷区域的12维频率能量参数和2维几何形状参数;用支持向量机进行木材表面缺陷类型的识别。采用Gabor变换和模糊C均值聚类方法对死节、活节、虫眼三种木材表面缺陷的分割精度都达到94%以上,支持向量机对缺陷类型分类正确率达到93%以上,这说明本文的方法对木材表面缺陷的分割与识别是可行的。
In order to recognize the wood surface defects of dead knot, live knot, and worm hole, Gabor transtorm and fuzzy C-means clustering algorithm were used to segment wood image defects. Mathematical morphology was used in post-processing operation of＂ segmented wood images, 12 frequency-enengy parameters and 2 shape parameters of defect targets were calculated. Support vector machines were used in the recognition of wood surface defect types.The segmentation accuracy to defects reached up to 94%, and the recognition accuracy to defect types of Support vector machines reached up to 93%. The result shows that it is feasible to segment and identify wood surface defects.
出处
《机电产品开发与创新》
2012年第3期79-81,共3页
Development & Innovation of Machinery & Electrical Products
基金
黑龙江省博士后基金项目资助(LBH-Q10160)
关键词
木材表面缺陷
缺陷分割
GABOR变换
支持向量机
wood surface defects
defects image segmentation
Gabor transtorm
support vector machine
