The Image Edge Extraction Algorithm Based on Hough Transform Coupling Ant Colony Optimization

XING Jing-yu; GAO Xu; HAN Pu

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The Image Edge Extraction Algorithm Based on Hough Transform Coupling Ant Colony Optimization

更新时间：2026-09-04

- The Image Edge Extraction Algorithm Based on Hough Transform Coupling Ant Colony Optimization
- Vol. 34, Issue 5, Pages: 83-88(2017)
- 作者机构：
  
  1. 南阳理工学院机构软件学院,河南,南阳,473000
  2. 中国科学院机构软件研究所,北京,100190
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
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邢静宇, 高需, 韩璞. 一种Hough变换与蚁群优化的云图像边缘检测算法[J]. 微电子学与计算机, 2017, 34(5): 83-88.

XING Jing-yu, GAO Xu, HAN Pu. The Image Edge Extraction Algorithm Based on Hough Transform Coupling Ant Colony Optimization[J]. 2017, 34(5): 83-88.
邢静宇, 高需, 韩璞. 一种Hough变换与蚁群优化的云图像边缘检测算法[J]. 微电子学与计算机, 2017, 34(5): 83-88. DOI：

XING Jing-yu, GAO Xu, HAN Pu. The Image Edge Extraction Algorithm Based on Hough Transform Coupling Ant Colony Optimization[J]. 2017, 34(5): 83-88. DOI：

摘要

针对云图像边缘检测算法中由于噪声的存在导致边缘定位精确度降低

边缘信息丢失和虚假边缘等问题

提出了一种Hough变换(Hough Transform

HT)耦合蚁群优化(Ant Colony Optimization

ACO)云图像边缘检测算法.先将需处理图像进行HT

消除噪声和线段间隔对图像边缘的影响; 其次

对图像像素梯度和像素圆形邻域统计均值的差值进行计算

定义二者之间的权重函数

并作为蚁群的信息素和启发信息; 最后

利用蚁群优化算法

引导蚁群搜索图像边缘

从而完成云图像边缘提取.实验表明: 与当前边缘检测算法比较

此方法能够有效降低噪声影响

获得的边缘完整、细节丰富

边缘优质系数高

算法效率高.

Abstract

In order to solve the image edge detection algorithm

the edge information is affected by the noise

causing the image edge positioning accuracy is low

Edge information loss and false edges and other issues

proposed a image edge extraction algorithm based on Hough transform (HT)coupling ant colony optimization(ACO).Firstly

introducing the Hough transform to the input image

eliminating the effect of noise and line segment. Secondly

the difference between the pixel gradient of the image and the statistical average of the circular neighborhood is calculated

and the weight function between the two is constructed

and the pheromone and the heuristic information are used as the ant colony. Finally

the ant colony optimization algorithm is used to guide the ant colony to search the edge of the image

so as to finish the image edge extraction. The simulation results show that compared with the current edge detection algorithm

the edge location accuracy and the edge details are abundant

which can effectively restrain the noise

and has better robustness.

关键词

Keywords

references

Marjan Kuchaki Rafsanjani, Zahra Asghari Varzaneh. Edge detection in digital images using Ant Colony Optimization[J]. Computer Science Journal of Moldova, 2015, 23(3): 343-360. DOI: 343

Swaminathan A, Ramapackiyam S S K. Edge detection for illumination varying images using wavelet similarity[J]. Let Image Processing, 2014, 8(5): 261-268. DOI: 261

张健, 何坤, 郑秀清. 基于蚁群优化的图像边缘检测算法[J]. 计算机工程, 2011, 37(17): 191-193. DOI: 191

Shih M Y, Tseng D C. Wavelet-based multiresolution edge detection and tracking[J]. Image and Vision Computing, 2015, 23(4): 441-451. DOI: 441

周激流, 詹晓倩, 何坤. 基于统计估计的图像边缘检测[J]. 沈阳工业大学学报, 2011, 32(6): 665-671. DOI: 665

张志龙, 杨卫平, 李吉成. 一种基于蚁群优化的显著边缘检测算法[J]. 电子与信息学报, 2014, 36(9): 2061-2067. DOI: 2061

高翔, 梁志伟, 徐国政. 基于Hough空间的移动机器人全局定位算法[J]. 电子测量与仪器学报, 2012, 26 (6): 484-490. DOI: 484

乔闹, 邹北骥, 邓磊. 一种基于图像融合的含噪图像边缘检测方法[J]. 光电子·激光, 2012, 33(11): 2215-2220. DOI: 2215

李敏花, 柏猛. 基于蚁群优化算法的复杂背景图像文字检测方法[J]. 计算机应用, 2011, 31(7): 1844-1846. DOI: 1844

李敏. 高分辨率合成孔径雷达图像高速公路检测法[J]. 计算机应用, 2011, 31 (7): 1825-1828. DOI: 1825

李俊山, 马颖, 赵方舟. 改进的Canny图像边缘检测算法[J]. 光子学报, 2011, 40(1): 50-54. DOI: 50

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