交叉熵约束的红外图像最小错误阈值分割

针对目标和背景具有相似统计分布的红外图像，经典阈值分割方法仅以某种形式的方差或熵作为准则，未考虑图像的实际特性，分割效果不甚理想。为此，提出了一种基于交叉熵约束的红外图像最小错误阈值分割新方法。首先，引入交叉熵来度量目标和背景统计分布的相似性，交叉熵越小表明分布越相似；然后在交叉熵小于一定值的条件下使分类错误达到最小。交叉熵的约束保证了分割过程适应红外图像实际特性，分类错误最小确保了分割效果的有效性。该方法原理清晰、参数设置简单，在一系列实际图像上的实验结果表明，与现有几种经典阈值分割方法相比，文中方法有效提高了目标和背景具有相似统计分布的红外图像的阈值分割准确率。

Minimum error thresholding for infrared image under constraint of cross entropy

Focusing on the infrared images which have similar statistical distributions between object and background, conventional thresholding methods only take variance or entropy by someway as criterions for thresholding selection and they don't consider actual characteristics of infrared images, so the segmentation results are unsatisfactory. In order to solve this problem, a novel method of cross entropy constrained minimum error thresholding of infrared image was proposed. Firstly, cross entropy was called to measure the similarity between object and background's statistical distributions. The smaller the cross entropy was, the more similar the distributions were. After that, classification error was minimized when the cross entropy was below a certain value. Constraint of cross entropy guaranteed the segmentation process fits actual characteristics of images. Meanwhile, minimum of classification error ensured the effectiveness of segmentation results. The principles of proposed method are clear and the parameter setting is simple. Experimental results on real images show that compared with several classic thresholding methods, the proposed method can improve thresholding segmentation accuracy of infrared image with similar statistical distributions between object and background effectively.