Improved Two-dimensional Maximum Entropy Segmentation Algorithm for Infrared Images Based on Fractal Theory
基于分形理论的改进型二维最大熵红外图像分割算法

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.