基于改进遗传算法的最大2维熵图像分割

为了解决传统最大2维熵分割算法计算量大、耗时较多等缺陷,提出一种基于改进遗传算法的最大2维熵图像分割法。通过对遗传算法变异操作方式进行改进,提高遗传算法寻找最大2维熵分割阈值的速度,加速分割算法对图像的分割,并进行了仿真实验验证。结果表明,改进模型的运行时间被压缩到了0.95s,远远低于传统的最大2维熵分割法。改进的分割方法实现了分割效率的提高,同时也保证了图像的分割精度。

Image segmentation of 2-D maximum entropy based on the improved genetic algorithm

In order to solve the defects of traditional maximum 2-D entropy segmentation algorithm, a large amount of calculation, more time consumption, and so on, a maximum 2-D entropy segmentation method based on the improved genetic algorithm was proposed. By improving the mutation operating mode of the genetic algorithm, the speed of the genetic algorithm to find maximum 2-D entropy segmentation threshold was improved, and then image segmentation by using the segmentation algorithm was accelerated.Through theoretical analysis and simulation experiments, the results show that, the running time of the modified model is compressed to 0.95s, which is far lower than the traditional maximum 2-D entropy segmentation method. The modified segmentation method improves the segmentation efficiency and ensures the accuracy of image segmentation.