摘要
针对现有二维Otsu图像分割算法未考虑到目标和背景这二类像素自身的内聚性,提出一种新的自适应二维Otsu算法。该算法通过待分割图像的二维直方图,分别统计类内的绝对差、类间总体离差以反映类内、类间的离散度,从而构造出新阈值判别函数。通过一种改进的遗传算法优化二维阈值判别函数,自动得到较理想的分割阈值。实验结果表明,与其它阈值判别函数相比,通过优化新的阈值判别函数得到的二维阈值,具有了较好的分割效果,能够更好地保留了目标物的轮廓,而且计算量小。
The present two-dimensional Otsu image segmentation algorithm ignores the cohesiveness of foreground and background pixels.A novel method of threshold recognition algorithm is proposed,which,by using two-dimensional histogram of the target image,counts the absolute difference within the cluster and the average total deviation between the cluster to reflect the scattered difference,and then constructs a new threshold recognition function.An improved genetic algorithm is adopted to optimize the new threshold recognition function so as to obtain the ideal threshold value automatically.Experimental results show that the two-dimensional threshold value obtained through the optimized threshold recognition function is of good segmentation efficiency,of good retaining of the object outline and of low work amount of calculation.
出处
《工程图学学报》
CSCD
北大核心
2011年第1期67-75,共9页
Journal of Engineering Graphics
基金
国家自然科学基金资助项目(60705015)
安徽省自然科学基金资助项目(070412054)
关键词
图像分割
二维OTSU法
阈值判别函数
类内最小差
类间最大差
image segmentation
two-dimensional Otsu algorithm
threshold recognition function
minimum within-cluster difference
maximum between-cluster difference
