高边界精度和抗噪的均值漂移图像分割算法  

Image segmentation algorithm based on mean shift with high boundary precision and anti-noise performance

作　　者：冷璐[1,2] 黎明[1] 张家树[2] 

机构地区：[1]南昌航空大学无损检测技术教育部重点实验室,江西南昌330063 [2]西南交通大学信号与信息处理四川省重点实验室,四川成都610031

出　　处：《计算机工程与设计》2010年第1期145-148,152,共5页Computer Engineering and Design

基　　金：国家自然科学基金项目(60475002);航空科学基金项目(2008ZD56003)

摘　　要：Meanshift(均值漂移)算法核函数窗口的带宽目前仍没有一个统一的确定标准。对整体图像根据Canny算子提取的边缘方向信息分成3类子图,一类是规则边缘子图,由规则边缘像素组成;第2类为非规则边缘子图,由边界方向变化剧烈的边缘像素组成;第3类是非边缘子图,由区域内部平坦区域和噪声区域组成。规则边缘子图和非边缘子图采用大窗口使区域内部更为平滑,并使噪声区域达到更高的抗噪性能,非规则边缘子图用小窗口可保持更高的边界精度。实验采用金属断口图像进行分割,结果表明,针对不同子图采用不同核函数带宽的方法使分割后的金属断口图像边界更准确,抗噪性能也更强。There is not an uniform standard of determining the bandwidth of window of kernel function for mean shift. Images are divided into three sub-images according to the orientation information of edges detected by canny operator. Regular edge sub-image contained regular edge pixels. Nonregular edge sub-image is composed of edge pixels whose orientation changed sharply. Non-edge sub-image consisted of inner pixels in smooth region and noise pixels. The bandwidth of kernel function window in regular edge sub-image and nonedge sub-image is large in order to smooth inner region and improve anti-noise performance. The bandwidth of kernel function window in nonregular edge sub-image is small so that the segmented image had more accurate boundary. The experimental results show that the method of selection bandwidth of kernel function window in different sub-image is effective to metal fracture images for more accurate boundary and better anti-noise performance.

关 键 词：均值漂移 子图 边界精度 抗噪性能 图像分割 金属断口 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]