木材彩色图像缺陷分割——基于Gabor滤波的改进C-V彩色模型

Segmentation for colored image of wood defect by improved C-V colored model of Gabor filter

分析了木材节子缺陷、单板节子的特点,提出了一种基于多通道Gabor滤波的改进C-V彩色模型的木材缺陷识别算法。该算法将彩色图像作为一个整体的图像,保留了图像的彩色信息。该算法利用多通道Gabor滤波器、K-均值聚类算法得到缺陷目标与背景的彩色区分图像;利用改进的彩色C-V模型对新图像进行边缘提取,得到理想的实验结果。与采用基于改进C-V模型与小波变换的灰度图像缺陷识别算法相对比,结果表明该方法可快速、准确地实现对木材节子缺陷彩色图像及单板多节子彩色图像的分割。

Through analyzing the features of wood defects and the features of veneer knots,this paper presents an identification algorithm of wood defects,which is based on improved C-V colored model of multi-channel Gabor filters.This algorithm,which protects the colored information of the image,makes the colored image be an integral image.The algorithm applies multi-channel Gabor filters and K-means clustering to getting colored image,which is the discrimination of the defective target and the background.The algorithm applies the improved C-V colored model on the new image to abstracting the new image＇s edge.To compare with the identification algorithm of gray image defects,which is based on improved C-V model and wavelet transformation,the results show that this method can segment the colored image of wood defects and the colored image of the multi-object veneer knot fast and accurately.