二维直方图斜分Tsallis–Havrda–Charvát熵图像阈值分割

本文指出了现有二维直方图区域直分法中存在明显的错分,提出了二维直方图区域斜分方法,即通过与主对角线平行的四条斜线将直方图分成内点区、边界点区和噪声点区,并按灰度级与邻域平均灰度级之和的大小进行分割.该方法可以运用于所有的基于二维直方图的阈值分割.文中导出了基于二维直方图区域斜分的Tsallis–Havrda–Charvát 熵阈值选取公式及其快速递推算法,给出了分割结果和运行时间.与基于二维直方图直分的Tsallis–Havrda–Charvát 熵原始算法相比,本文提出的基于二维直方图斜分的Tsallis–Havrda–Charvát 熵阈值分割算法,使分割后的图像内部区域均匀,边界形状准确,更有稳健的抗噪性,其运行时间减少了五个数量级.

Tsallis–Havrda–Charvát Entropy Image Thresholding Based on Two-dimensional Histogram Oblique Segmentation

This paper points out the obvious wrong segmentation in the existing two-dimensional histogram vertical segmentation method. A new method of two-dimensional histogram oblique segmentation is proposed, in which the histogram is divided into inner, edge and noise parts by four oblique lines paralleled with the main diagonal. The image is segmented according to the sum of point grayscale and neighborhood average grayscale. The method could be used in almost all the two-dimensional histogram threshold algorithm...