Research on chaotic ant colony algorithm in image edge detection

Computer Engineering and Applications ›› 2015, Vol. 51 ›› Issue (2): 194-197.

Previous Articles Next Articles

Research on chaotic ant colony algorithm in image edge detection

GENG Yanxiang1，2, SUN Yunshan1，2, XIE Jingpeng1, LIU Chao1

1.School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
2.School of Electric Information Engineering, Tianjin University, Tianjin 300072, China

Online:2015-01-15 Published:2015-01-12

混沌蚁群算法在图像边缘检测中的应用

耿艳香1，2，孙云山1，2，谢靖鹏1，刘超1

1.天津商业大学信息工程学院，天津 300134
2.天津大学电子信息工程学院，天津 300072

Abstract

Abstract: Chaos ant colony algorithm is applied in image edge detection, because of its randomness, ergodicity and positive feedback. The update of the pheromone matrix by chaos ant colony algorithm is used to calculate the threshold to get the edge of image, and then it can fully and quickly find the edge of image to avoid falling into the optimal value. The algorithm improves the continuity and accuracy of image edge detection. Simulation results of the computer show that chaos ant colony algorithm to obtain more completely and clearly edge, which is obtained better effect.

Key words: chaos ant colony algorithm, image segmentation, edge detection

摘要： 运用混沌蚁群算法进行图像的边缘检测是针对混沌蚁群算法具有随机性、遍历性、正反馈性，通过更新信息素矩阵来计算图像阈值，从而获得图像边缘信息，能够更全面、迅速地找到图像的边缘，避免过早陷入局部最优，提高了图像边缘检测的连续性和准确性。计算机仿真实验表明，通过混沌蚁群算法获得的图像边缘更加完整和清晰，取得了较好的效果。

关键词: 混沌蚁群算法, 图像分割, 边缘检测

GENG Yanxiang1，2, SUN Yunshan1，2, XIE Jingpeng1, LIU Chao1. Research on chaotic ant colony algorithm in image edge detection[J]. Computer Engineering and Applications, 2015, 51(2): 194-197.

耿艳香1，2，孙云山1，2，谢靖鹏1，刘超1. 混沌蚁群算法在图像边缘检测中的应用[J]. 计算机工程与应用, 2015, 51(2): 194-197.

[1]	SHI Chuntian, ZENG Yanyang, HOU Shouming. Summary of Application of Swarm Intelligence Algorithms in Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 36-47.
[2]	ZHAO Yang, ZHANG Junhua. Multi-scale Feature Fusion Method for Spinal X-Ray Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 214-219.
[3]	PAN Peixin, PAN Zhongliang. Active Contour Image Segmentation Combined with Saliency [J]. Computer Engineering and Applications, 2021, 57(8): 225-230.
[4]	DONG Peng, ZHOU Feng, ZHAO Congcong, WANG Yafei, MI Zetian, FU Xianping. Automatic Measurement of Underwater Sea Cucumber Size Based on Binocular Vision [J]. Computer Engineering and Applications, 2021, 57(8): 271-278.
[5]	PENG Jing, LUO Haoyu, ZHAO Gansen, LIN Chengchuang, YI Xusheng, CHEN Shaojie. Survey of Medical Image Segmentation Algorithm in Deep Learning [J]. Computer Engineering and Applications, 2021, 57(3): 44-57.
[6]	MA Qinglu, TANG Xiaoyao. Vehicle Detection Algorithm Combining Time Domain and Watershed Information [J]. Computer Engineering and Applications, 2021, 57(24): 227-233.
[7]	LIU Rui, DING Hui, SHANG Yuanyuan, SHAO Zhuhong, LIU Tie. COVID-19 Medical Imaging Dataset and Research Progress [J]. Computer Engineering and Applications, 2021, 57(22): 15-27.
[8]	QI Xiaoxiang, LI Min, ZHU Ying, SONG Yu, DU Weidong. Adaptive Region Segmentation of SAR Image Based on Edge Detection [J]. Computer Engineering and Applications, 2021, 57(22): 232-240.
[9]	ZHOU Shaoguang, WU Hao, ZHAO Chanjuan, CHEN Renxi. Transfer Learning for Hyperspectral Image Classification Using Homogeneous Area Characteristics [J]. Computer Engineering and Applications, 2021, 57(21): 224-233.
[10]	LI Jiasheng, GUO Shuxu, ZHANG Lei, ZHENG Shuang, ZHANG Huimao, QIU Yunhai, LI Xueyan. Research of Segmentation and Detection of Liver Metastases from CT Images Based on Deep Network [J]. Computer Engineering and Applications, 2021, 57(2): 191-197.
[11]	DING Cheng, WENG Liguo, XIA Min, CUI Yichen, QIAN Junhao, LIU Jia. Multi-attention Mechanism Network Satellite Image Segmentation Algorithm [J]. Computer Engineering and Applications, 2021, 57(2): 223-229.
[12]	LI Zhenqiang, WANG Shucai, ZHAO Shida, BAI Yu. Cutting Methods of Sheep’s Trunk Based on Improved DeepLabv3+ and XGBoost [J]. Computer Engineering and Applications, 2021, 57(18): 263-269.
[13]	SUN Weijie, YANG Jun. Research on Remote Sensing Image Segmentation Based on Improved Simple Non-iterative Clustering [J]. Computer Engineering and Applications, 2021, 57(13): 185-192.
[14]	YANG Yong, GUO Ling, YE Yangdong. Approach of Total Variation Flow Boundary Integrated with M2GGD for Natural Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(11): 202-210.
[15]	LU Yuanyuan, FENG Hao, LI Jing. Active Contour Image Segmentation Combined with Statistical Modeling of Distribution Metrics [J]. Computer Engineering and Applications, 2020, 56(7): 228-233.

Research on chaotic ant colony algorithm in image edge detection

混沌蚁群算法在图像边缘检测中的应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics