广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (05): 47-55.doi: 10.12052/gdutxb.220148

• 计算机科学与技术 • 上一篇    

基于ISSA和积分图的二维熵图像多阈值分割快速算法

吴圳桦1, 唐文艳1, 吕文阁1, 陈汝杰1, 侯梦华2, 李德源1   

  1. 1. 广东工业大学 机电工程学院，广东 广州 510006;
    2. 深圳市启灵图像科技有限公司，广东 深圳 518114
  • 收稿日期:2022-09-22 发布日期:2023-09-26
  • 通信作者: 吕文阁(1966-),男,教授,博士,主要研究方向为启发式优化算法、机器视觉应用等,E-mail:lvwenge@gdut.edu.cn
  • 作者简介:吴圳桦(1997-),男,硕士研究生,主要研究方向为机器视觉算法与应用
  • 基金资助:
    国家自然科学基金资助项目(51776044)

Fast Image Segmentation with Multilevel Threshold of Two-dimensional Entropy Based on ISSA and Integral Graph

Wu Zhen-hua1, Tang Wen-yan1, Lyu Wen-ge1, Chen Ru-jie1, Hou Meng-hua2, Li De-yuan1   

  1. 1. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China;
    2. Shenzhen QiLing Image Technology Co., Ltd., Shenzhen 518114, China
  • Received:2022-09-22 Published:2023-09-26

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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。

关键词: 多阈值分割, 麻雀搜索算法, 积分图, 机器视觉

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.

Key words: multilevel threshold segmentation, sparrow search algorithm, integral graph, machine vision

中图分类号: 

  • TP391.4
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