基于分形理论的改进型二维最大熵红外图像分割算法

提出了一种基于分形理论的改进型二维最大熵红外图像阈值分割算法。该算法利用图像分形维数挖掘像素的空间分布信息,然后将原图像灰度及其分形维数映射图像灰度相结合组成二维随机向量,并构造出联合离散概率分布。在此基础上,以二维最大熵原则来确定一个最佳二维分割阈值,进而取得分割结果。实验结果表明,该算法在分割效果上优于传统的二维最大熵分割算法。

Improved Two-dimensional Maximum Entropy Segmentation Algorithm for Infrared Images Based on Fractal Theory

An improved two-dimensional maximum entropy segmentation algorithm for infrared images based on fractal theory was proposed. The algorithm uses the image fractal dimension to excavate the spatial distribution information in image pixels. Then, it combines the original image grayscale with the mapped image grayscale from the fractal dimension to form a two-dimensional random vector and construct its joint probability distribution. On that basis, the two-dimensional maximum entropy principle is used to determine an optimal two-dimensional segmentation threshold and hence to obtain the final segmentation result. The experimental result shows that the improved algorithm is better than the traditional two-dimensional maximum entropy segmentation algorithm in segmentation effectiveness.