提出基于二维Arimoto熵的阈值分割方法.首先由图像的像素值及其邻域像素均值得到图像的二维直方图,然后从二维直方图中计算出二维Afimoto熵.当二维Afimoto熵达到最大时,对应的灰度级对即为分割阈值.通过引入二维联合幂概率分布建立快速算法,使算法速度大大提高,易于硬件实现.大量的对比实验表明,本文算法表现稳定,总体的分割效果优于基于二维Renyi熵和二维Shannon熵的阈值分割算法.
A thresholding technique is proposed based on two-dimensional Arimoto entropy. Firstly, a two-dimensional histogram is determined by the gray value and the local average gray value of the pixels. Then, the two-dimensional Arimoto entropy is obtained from the two-dimensional histogram. The pair of gray values which makes the two-dimensional Arimoto entropy largest is the thresholding. By introducing in a two-dimensional joint power-probability distribution, a fast algorithm is proposed. The fast algorithm speeds up the implementation and makes the method suitable to real-time systems. Experiments indicate that the thresholding method based on two-dimensional Arimoto entropy gives a steady performance and it is better than the methods based on Renyi entropy and Shannon entropy.