二维直方图区域斜分的最大熵阈值分割算法

Maximum Entropy Image Thresholding Based on Two-Dimensional Histogram Oblique Segmentation

指出现有二维直方图区域直分法中存在明显的错分,提出一种二维直方图区域斜分方法.导出基于二维直方图区域斜分的最大熵阈值选取公式及其快速递推算法,给出图像分割结果和运行时间.并与基于二维直方图直分的最大熵原始算法及其快速算法进行比较.结果表明二维直方图区域斜分可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.本文算法的运行时间约为二维直方图斜分最大熵法原始算法的2%,不到二维直方图直分最大熵法的两种快速递推算法的1/3.

The obvious wrong segmentation is pointed out in the existing two-dimensional histogram vertical segmentation method. A two-dimensional histogram oblique segmentation method is proposed. Then the formula and its fast recursive algorithm of the maximum Shannon entropy thresholding are deduced based on the two-dimensional histogram oblique segmentation. Finally, the threshold images and the processing time are given in the experimental results and analysis. The results are compared with those of the original maximum Shannon entropy algorithm and its fast algorithms based on the two-dimensional histogram vertical segmentation. The experimental results show that the proposed method makes the inner part uniform and the edge accurate in the threshold image, and it has a better anti-noise property. The processing time of the fast recursive algorithm of the proposed method is about 2% of that of the original two-dimensional maximum Shannon entropy algorithm, and it is less than one third of that of two fast recursive algorithms of the maximum Shannon entropy thresholding based on the two-dimensional histogram vertical segmentation.