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中文摘要：
鉴于常用二维直方图区域直分法存在错分,最近提出的斜分法不具普遍性,提出了适用面更广的基于二维直方图θ划分和最大Shannon熵的图像阈值分割算法.首先给出了二维直方图θ划分方法,采用四条平行斜线及一条其法线与灰度级轴成θ角的直线划分二维直方图区域,按灰度级和邻域平均灰度级的加权和进行阈值分割,斜分法可视为该方法中θ=45°的特例;然后导出了二维直方图θ-划分最大Shannon熵阈值选取公式及其快速递推算法;最后给出了θ取不同值时的分割结果及运行时间,θ取较小值时,边界形状准确性较高,θ取较大值时,抗噪性较强,应用时可根据实际图像特点及需求合理选取θ的值.与常规二维直方图直分最大Shannon熵法相比,本文提出的方法所得分割结果更为准确,抵抗噪声更为稳健,且所需运行时间及存储空间也大为减小.
英文摘要：
In view of the obvious wrong segmentation in commonly used region division of 2-D histogram and the non- universality of oblique segmentation method for image thresholding proposed recently,in this paper a much more widely suitable thresholding method is proposed based on the θ-division of 2-D histogram and the maximum Shannon entropy criterion. Firstly,the θ-division method of 2-D histogram is given. The region is divided by four parallel oblique lines and a line,where the angle between its normal line and gray level axis is θ degrees. Image thresholding is performed according to pixel’s weighted average value of gray level and neighbour average gray level. The oblique segmentation method can be regarded as a special case of the proposed method at θ = 45°. Then the formulae and its fast recursive algorithm of the method are deduced. Finally the segmented results and the running time at different values of θ are listed,which show that the segmented images achieve more accurate borders at smaller values of θ and the anti-noise is better at larger values of θ. The value of θ can be selected according to the real image characteristics and the requirements of segmented results. Compared with the algorithm of conventional 2-D maximum Shannon entropy method,the proposed method not only achieves more accurate segmentation results and more robust anti-noise,but also reduces the running time and memory space significantly.
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