二维Arimoto灰度熵阈值分割

Two-Dimensional Arimoto Gray Entropy Thresholding

针对近年提出的二维Arimoto熵阈值分割方法只依赖图像灰度级出现的频数信息,而未考虑图像类内灰度均匀性这一问题,提出了基于灰度-梯度直方图的二维Arimoto灰度熵阈值分割方法.首先,在Arimoto熵的基础上直接考虑图像类内灰度均匀性,构建出一维Arimoto灰度熵阈值选取公式;结合灰度-梯度二维直方图目标与背景区域划分方式,推导出二维Arimoto灰度熵阈值选取公式;通过阈值选取函数所涉及中间变量的递推计算公式来消除冗余计算;采用基于Tent映射的混沌序列对人工蜂群算法的局部搜索阶段进行改进,以改进后的蜂群优化算法来加快图像分割最佳阈值的搜索速度,大大减少了时间花费.大量的典型图像对比实验结果表明,所提出的方法能够快速而准确地实现图像分割,且总体效果优于二维Shannon熵、二维Tsallis灰度熵和二维Arimoto熵阈值分割方法.

A recently proposed 2D Arimoto entropy thresholding method only depends on frequency information of gray scale in an image,without considering uniformity of within-class gray scales.To solve this problem,a 2D Arimoto gray entropy thresholding method based on gray scale-gradient histogram is proposed.Uniformity of within-class gray scale is considered based on Arimoto entropy and a formula for 1D Arimoto gray entropy threshold selection constructed.Using regional division of object and background in a gray scale-gradient 2D histogram,a formula for 2D Arimoto gray entropy threshold selection is derived.Recursion formulae of intermediate variables in the threshold selection criterion function are used to eliminate redundant computation.The local period of an artificial bee colony algorithm is improved using a chaotic sequence based on tent mapping.The improved bee colony optimization algorithm can accelerate search speed of the optimal threshold for image segmentation to significantly reduce execution time.Experimental results based on a large number of typical images show that the proposed method can segment image quickly and accurately,with the overall performance better than 2D Shannon entropy thresholding,Tsallis gray entropy thresholding,and Arimoto entropy thresholding.