【摘要】 为提高二维熵图像多阈值分割的性能，使其能够满足工业使用当中的实时性要求，本文提出了基于改进麻雀搜索算法(Improved Sparrow Search Algorithm, ISSA)和积分图的二维熵图像多阈值分割快速算法。首先，引入麻雀搜索算法(Sparrow Search Algorithm, SSA)并对该算法的算法性能进行分析研究，针对SSA存在的全局搜索能力差、容易陷入局部最优解的缺点，提出了基于方差线性递减的高斯扰动策略和随机步长移动策略的改进麻雀搜索算法(ISSA)。接着，进一步地引入积分图方法，降低总信息熵的运算量，并将总信息熵作为ISSA的适应度函数进行最佳阈值寻优，提出了基于ISSA并结合积分图的二维熵图像多阈值分割快速算法。最后，使用该方法与现有分割算法进行对比实验，实验结果表明，本文方法提升了图像二维熵多阈值分割的分割效率，同时在工业应用场景仍能够获得相同的效果。更多还原

【Abstract】 In order to improve the performance and efficiency of image segmentation with multilevel threshold of two-dimensional entropy for practical industrial applications, this paper proposes a fast image segmentation method with multilevel threshold of two-dimensional entropy based on ISSA and integral graph. Firstly, we introduce and analyze the sparrow search algorithm(SSA). To address the shortcomings of SSA, such as poor global search ability and easy to fall into local optimal solution, we propose an improved sparrow search algorithm(ISSA) based on Gaussian perturbation strategy with linear decreasing variance and moving strategy with random step size. Then,we further introduce the integral graph method to reduce the calculation amount of the entropy, use the entropy as the fitness function of ISSA to search the optimal threshold, and propose a fast algorithm for image segmentation with multilevel threshold of two-dimensional entropy based on ISSA and integral graph. Finally, we compare the proposed method with the existing segmentation algorithms, and the experimental results show that the proposed method improves the segmentation efficiency of image segmentation with multilevel threshold of two-dimensional entropy in industrial application scenarios.更多还原