一种新的自适应二维Otsu图像分割算法研究

A new adaptive two-dimensional Otsu image segmentation algorithm research

现有二维Otsu图像分割算法的阈值识别函数是通过计算类间离散度矩阵的迹来实现的,没有考虑目标和背景这两类像素自身的内聚性且计算复杂度高,为此提出一种新的阈值识别函数设计算法.该算法先统计待分割图像目标类和背景类各自类内的绝对差,相加得到总体类内绝对差之和;再统计目标类和背景类两类之间的总体平均离差;然后把总体类内绝对差和类间总体离差的商式作为阈值识别函数.实验结果表明,与现有的识别函数相比,利用新构造的阈值识别函数来自适应寻优阈值,从主观上和客观上都取得了较好的分割效果,而且计算量较小.

The threshold recognition function of existing two-dimensional Otsu image segmentation algorithms is based on the trace count of between-cluster scattered measure matrix but they do not consider the cohesiveness of foreground and background pixels themselves and are too complex. Therefore, a novel algorithm with a new threshold recognition function was proposed. The algorithm counted the absolute differences regarding the class of object and background of an object image and added them up to obtain the sum of total within-cluster absolute difference. Finally the algorithm set the quotient of the sum of total within-cluster absolute difference and the total deviation as the threshold recognition function. Experiment results show the efficiency in segmentation and low cost in computation of the proposed algorithm with a newly defined threshold recognition function by subjectively and objectively comparing to the existing threshold recognition functions.