狼群优化的二维Otsu快速图像分割算法

传统二维Otsu算法的阈值选取大都采用穷尽搜索方式，造成算法分割时间较长、实时性差等缺点，影响图像分割效果。为提高算法的运行效率，采用狼群算法来搜索最优阈值，每匹人工狼代表一个可行的二维阈值向量，狼群通过游走、召唤、围攻这三种智能行为的不断迭代以及狼群间的信息交互来获取最佳阈值。仿真结果表明，与标准粒子群优化二维Otsu算法和传统二维Otsu算法相比，狼群优化算法降低了分割时间并提高了图像分割精度。

A fast two-dimensional Otsu image segmentation algorithm based on wolf pack algorithm optimization

Threshold selection of traditional two dimensional Otsu algorithm generally depends on the exhaustive search method. However it cannot be applied to real time systems for its long segmentation time and poor real time performance which affect the efficiency of image segmentation. In order to reduce the running time of the two dimensional Otsu algorithm, we use the wolf pack algorithm to find the best threshold vector. Each artificial wolf represents a feasible two dimensional threshold vector. And the wolves get the best threshold through constant iteration of intelligent behaviors, including scouting behaviors, summoning behaviors and beleaguering behaviors, as well as the communication information among wolves. Simulation results show that compared with the two dimensional Otsu algorithm with standard PSO optimization and the traditional two dimensional Otsu algorithm, the proposed algorithm can reduce segmentation time and improve the accuracy of image segmentation.