摘要
最大散度差法是经典的Otsu法一种很好改进算法,为了提高它在图像受到噪声干扰或光照不均匀时的分割准确性,现提出一种基于二维直方图斜分的最大散度差法,该算法不仅综合考虑了类间散度及类内散度对图像信息分类的作用,同时还利用图像空间区域信息以提高抗噪声能力,为减少计算量、提高分割速度,文中给出了快速递推算法,实验结果表明该算法比二维斜分Otsu法、二维斜分最大熵法等算法具有更准确的分割效果、更强的抗噪声能力,同时运行时间更少。
Maximum scatter difference method is a good improved algorithm for the classical Otsu method. In order to improve the algorithm＇s segmentation accuracy when the image is interfered by the noise or uneven illumination, a new maximum scatter difference image thresholding segmentation algorithm base on two-dimensional histogram oblique （TOMSD） is proposed. This algorithm considers the impact of between-class divergence and within-class divergence on the image information classification, and takes advantage of image space information to improve the ability of anti- noise. The fast recursion algorithm is given to speed up computational time. The results show that TOMSD has more accurate segmentation effect and a better anti-noise property than the two-dimensional histogram oblique Otsu method and two-dimensional histogram oblique maximum entropy method, and the running time is less.
出处
《激光与红外》
CAS
CSCD
北大核心
2014年第4期463-468,共6页
Laser & Infrared
基金
湖南省教育厅科研项目(No.10C1263)
湘潭大学科研项目(No.11QDZ11)资助课题
关键词
阈值分割
最大散度差
二维直方图区域斜分
快速递推算法
image thresholding
maximum scatter difference
two-dimensional histogram oblique segmentation
fast re- cursion algorithm
