基于混沌遗传算法的二维最大熵图像分割

研究了基于二维最大熵的图像分割算法，针对基于二维最大熵的图像分割算法存在的计算复杂度高、计算时间长等问题，提出了一种基于混沌遗传算法的二维最大熵算法。该方法利用类似载波的方法将混沌序列映射至双阈值的二维空间，之后利用混沌遗传算法搜索最佳阈值进行图像分割。实验结果表明，由于该方法考虑了点灰度和区域灰度均值，且采用了有效的全局搜索算法，所以不仅得到了令人满意的分割效果，而且大大提高了计算速度，是一种实用有效的图像分割方法。

2-D Maximum Entropy Method in Image Segmentation Based on Chaos Genetic Algorithm

The 2-D maximum entropy image segmentation method is studied in this paper,for the problems that the method is complex,time-consuming and lack of practicability during evaluating threshold,a 2-D maximum entropy image segmentation method based on CGA(chaos genetic algorithm) is presented.The method searches for all the local maximal thresholds in the way of mapping from chaos sequences to 2-D variables space which is similar to carrying waves.The experiment results indicate that the proposed method can not only obtain the perfect performance of segmentation but also greatly improve the speed of computation due to considering the gray scale value of pixel and the gray scale mean value of region as well as adopting global search algorithm.So it is a practical and effective method of image segmentation.