Realization of infrared image segmentation based on improved Chan-Vese model

doi:10.3778/j.issn.1002-8331.2009.28.059

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (28): 196-198.DOI: 10.3778/j.issn.1002-8331.2009.28.059

• 图形、图像、模式识别 • Previous Articles Next Articles

Realization of infrared image segmentation based on improved Chan-Vese model

ZHANG Yi-xiong¹，LI Xi-ying^2，3

1.Physics and Electronic Engineering Department，Hanshan Normal University，Chaozhou，Guangdong 521041，China
2.School of Engineering，Sun Yat-sen University，Guangzhou 510275，China
3.State Key Laboratory of Optoelectronic Materials and Technologies，Sun Yat-sen University，Guangzhou 510275，China

Received:2009-04-14 Revised:2009-06-08 Online:2009-10-01 Published:2009-10-01
Contact: ZHANG Yi-xiong

Chan-Vese可变模块法快速实现红外图像分割

张奕雄¹，李熙莹^2，3

1.韩山师范学院物理与电子工程系，广东潮州 521041
2.中山大学工学院，广州 510275
3.中山大学光电材料与技术国家重点实验室，广州 510275

通讯作者: 张奕雄

Abstract

Abstract: This paper analyzes the speed of the Chan-Vese method.However，as the simplified model of Mumford-Shah，Chan-Vese method will result in large amount of calculation，seriously impact the speed of curve deformation，due to the full-range changes of level set function in Chan-Vese model processing.This paper draws on the variable masking method to raise the speed of Chan-Vese model.The experiment result of the infrared image segmentation demonstrates the efficiency and effectiveness of the improved method.

Key words: improved Chan-Vese method, infrared image, image segmentation

摘要： Chan-Vese方法作为Mumford-Shah的简化模型，因其水平集函数在演化过程中的全区域变化，会造成计算量极大，严重影响曲线形变速度；即使采用窄带法，在像素大的图像处理中，演化速度也极大地受到限制。提出一种改进Chan-Vese可变模块法快速实现红外图像分割，提出了形变曲线的不同节点根据前一次迭代演化情况，采用不同大小的模块运算，从而极大地提高了Chan-Vese模型演化速度。通过对红外图像的实验仿真，证明了该改进C-V方法能达到理想的效果。

关键词: 改进Chan-Vese方法, 红外图像, 图像分割

CLC Number:

T391.41

ZHANG Yi-xiong¹，LI Xi-ying^2，3. Realization of infrared image segmentation based on improved Chan-Vese model[J]. Computer Engineering and Applications, 2009, 45(28): 196-198.

张奕雄¹，李熙莹^2，3. Chan-Vese可变模块法快速实现红外图像分割[J]. 计算机工程与应用, 2009, 45(28): 196-198.

[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]	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.
[7]	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.
[8]	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.
[9]	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.
[10]	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.
[11]	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.
[12]	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.
[13]	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.
[14]	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.
[15]	ZHANG Shujie, ZHENG Liping, HAN Qing, ZHANG Han. Pictures to Clothes：Method of Quickly Generating Costumes for Virtual Characters [J]. Computer Engineering and Applications, 2020, 56(7): 234-239.

Realization of infrared image segmentation based on improved Chan-Vese model

Chan-Vese可变模块法快速实现红外图像分割

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics