Chan-Vese model with gray level transformation

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (23): 157-158.

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

Chan-Vese model with gray level transformation

LUO Defang1，2，HE Chuanjiang1，YUAN Ye1

1.Department of Information and Computing Science，College of Mathematics and Statistics，Chongqing University，Chongqing 401331，China
2.Chongqing Normal University，Chongqing 400700，China

Received:1900-01-01 Revised:1900-01-01 Online:2011-08-11 Published:2011-08-11

结合灰度变换的Chan-Vese模型

罗德芳1，2，何传江1，原野1

1.重庆大学数学与统计学院信息与计算科学系，重庆 401331
2.重庆师范大学，重庆 400700

Abstract

Abstract: Power-law transformation and negative transformation are two basic types of gray level transformations that are used frequently for image enhancement.This paper incorporates the two transformations into the well-known Chan-Vese model to raise the performance of Chan-Vese model in terms of segmentation speed and quality.By practical experiments，it is verified that new Chan-Vese model has faster convergence than Chan-Vese model，and exhibits certain capability of handling straight edges and corners.

Key words: image segmentation, active contour models, Chan-Vese model, power-law transformation, negative transformation

摘要： 幂变换和负片变换是图像增强中频繁使用的两种基本的灰度变换。该文把这两种灰度变换与Chan-Vese模型进行结合，以提高Chan-Vese模型的分割速度和效果。实验表明：该方案大大提高了Chan-Vese模型的收敛速度，而且也使Chan-Vese模型具有较好的处理直线和尖角的能力。

关键词: 图像分割, 活动轮廓模型, Chan-Vese模型, 幂变换, 负片变换

LUO Defang1，2，HE Chuanjiang1，YUAN Ye1. Chan-Vese model with gray level transformation[J]. Computer Engineering and Applications, 2011, 47(23): 157-158.

罗德芳1，2，何传江1，原野1. 结合灰度变换的Chan-Vese模型[J]. 计算机工程与应用, 2011, 47(23): 157-158.

[1]	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.
[2]	PAN Peixin, PAN Zhongliang. Active Contour Image Segmentation Combined with Saliency [J]. Computer Engineering and Applications, 2021, 57(8): 225-230.
[3]	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.
[4]	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.
[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.

Chan-Vese model with gray level transformation

结合灰度变换的Chan-Vese模型

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics