基于蚁群算法的二维最大熵分割算法

由于二维最大熵分割法不仅考虑了像素的灰度信息,而且还充分利用了像素的空间邻域信息,因此能够取得较好的分割效果.但是,该方法的计算量巨大,不利于红外图像的快速处理.蚁群算法于20世纪90年代初提出,是受到蚁群集体行为的启发而提出的一种基于种群的模拟进化算法,属于随机搜索算法.该算法已经成功应用于旅行商等离散问题.将蚁群算法应用于二维最大熵法,提出了基于蚁群算法的二维最大熵分割算法.与传统的穷尽搜索法相比,求解速度提高了60倍左右.仿真实验表明,该方法快速、简单、有效.

Two-Dimensional Maximum Entropy Segmentation Based on Ant Colony Optimization

The 2-D maximum entropy method reflects information of the gray distribution and space-related information of the neighborhood. Therefore the segmentation result is more accurate than the 1-D method. However its computational cost is an obstacle in application. Ant Colony Optimization is has been successfully applied to some discrete problems, such as the traveling salesman problem. The ant colony optimization is introduced and the 2-D maximum entropy segmentation is presented based on ant colony optimization. Through the experiments of segmenting infrared images, it is about 60 times faster than the traditional exhaustive search algorithm. The proposed algorithm has been proved to be fast, simple and effective.