利用二维属性直方图的最大熵的图像分割方法

提出二维属性直方图的概念。它是一种由先验知识约束的二维直方图,可以使一些图像处理方法得到简化和变得可行。在此基础上提出一种基于二维属性直方图的图像分割方法。该方法步骤是构造图像的属性集,确定相应的二维属性直方图,然后利用二维属性直方图的最大熵法确定灰度阈值。为了说明该方法的性能,将其用于一种海底小目标图像分割。同时,也使用一维属性直方图的最大熵分割法。结果表明该方法比一维属性直方图的最大熵法抗干扰性更强,分割效果更好。二维属性直方图的概念具有理论意义与应用价值。该方法适用于图像有某种先验知识的场合。

Image Segmentation Using the Maximum Entropy of the Two-Dimensional Bound Histogram

The concept of the two-dimensional bound histogram(TDBH) is proposed.It is the two-dimensional histogram bound by some prior knowledge,and it can make some image processing methods simple and feasible.Furthermore,an image segmentation method based on the TDBH is presented.In the method,the bound set of an image and its corresponding TDBH are constructed,and the gray-level threshold for segmentation is determined according to the maximum entropy of the TDBH.To show the validity of the method,it is used in segmenting of the image of a small underwater target.The image segmentation method based on the maximum entropy of the one-dimensional bound histogram(ODBH) is also used.The results show that the former,the proposed method,has better antinoise performances and segmental effects than the latter.The concept of the TDBH is significant in theory and engineering.The proposed method is applicable to the image where there is some prior knowledge.