New method for edge detection of infrared images based on Mumford-Shah model

fang liang; lu jia-jia; ye yu-tang; yang xian-ming; cheng zhi-qiang

Volume 19 Issue 04

Apr. 2007

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > High Power Laser and Particle Beams > 2007 > 19(04): 0-

fang liang, lu jia-jia, ye yu-tang, et al. New method for edge detection of infrared images based on Mumford-Shah model[J]. High Power Laser and Particle Beams, 2007, 19.

Citation:

fang liang, lu jia-jia, ye yu-tang, et al. New method for edge detection of infrared images based on Mumford-Shah model[J]. High Power Laser and Particle Beams, 2007, 19.

fang liang, lu jia-jia, ye yu-tang, et al. New method for edge detection of infrared images based on Mumford-Shah model[J]. High Power Laser and Particle Beams, 2007, 19.

Citation:

fang liang, lu jia-jia, ye yu-tang, et al. New method for edge detection of infrared images based on Mumford-Shah model[J]. High Power Laser and Particle Beams, 2007, 19.

PDF( 845 KB)

New method for edge detection of infrared images based on Mumford-Shah model

1.
School of Opto-electronic Information,University of Electronic Science and Technology of China,Chengdu 610054,China

Publish Date: 2007-04-15

Abstract

Abstract

Chan-Vese(C-V) model is an excellent model of simplified Mumford-Shah models. As Chan-Vese model is based on two homogenous regions, it causes failure in edge detection of infrared images. A level set method for edge detection of infrared image based on improved Mumford-Shah model is proposed. The schemes of preserving object edges, establishing the stopping criterion and speeding up the algorithm are discussed. The experiments show that this model can overcome the failure of C-V model and detect edge of infrared images efficiently.
- mumford-shah model,
- chan-vese model,
- infrared images,
- edge detection

FullText(HTML)

References(0)

References

Relative Articles

[1]	Qian Kun, Yang Junyan, Yu Yue, Zhao Dong, Rong Shenghui. Infrared target tracking based on selective convolution features[J]. High Power Laser and Particle Beams, 2019, 31(9): 093202. doi: 10.11884/HPLPB201931.190133
[2]	You Anqing, Wang Lei, Zhang Rong, Song Haifeng. Detecting approximately-circular object in image[J]. High Power Laser and Particle Beams, 2015, 27(12): 121004. doi: 10.11884/HPLPB201527.121004
[3]	Qu Shiru, Yang Honghong. Infrared image segmentation based on PCNN with genetic algorithm parameter optimization[J]. High Power Laser and Particle Beams, 2015, 27(05): 051007. doi: 10.11884/HPLPB201527.051007
[4]	Wang Xiaoyang, Peng Zhenming, Zhang Ping, Meng Yeming. Infrared small dim target detection based on local contrast combined with region saliency[J]. High Power Laser and Particle Beams, 2015, 27(09): 091005. doi: 10.11884/HPLPB201527.091005
[5]	Ling Qiang, Huang Shucai, Wu Xiao, Zhong Yu. Infrared small target detection based on kernel anisotropic diffusion[J]. High Power Laser and Particle Beams, 2015, 27(01): 011014. doi: 10.11884/HPLPB201527.011014
[6]	He Ming, Wang Yadi, Wang Xinsai, Lu Jianfang, Zhang Yi, Li Zhijun. Adaptive bilocal enhancement algorithm for infrared image with extreme temperature difference[J]. High Power Laser and Particle Beams, 2014, 26(08): 081006. doi: 10.11884/HPLPB201426.081006
[7]	Bai Honggang, Zhang Jianqi, Wang Xiaorui. Infrared complex background clutter suppression algorithm based on wave atom transform[J]. High Power Laser and Particle Beams, 2013, 25(01): 37-41. doi: 10.3788/HPLPB20132501.0037
[8]	Ma Ke, Peng Zhenming, He Yanmin, Gao Yuan, Zhang Ping. An improved method for dim infrared target detection with nonsubsampled Contourlet transform[J]. High Power Laser and Particle Beams, 2013, 25(11): 2811-2815. doi: 10.3788/HPLPB20132511.2811
[9]	Jing Liang, Peng Zhenming, He Yanmin, Pu Tian. Infrared dim target detection based on anisotropic SUSAN filtering[J]. High Power Laser and Particle Beams, 2013, 25(09): 2208-2212. doi: 10.3788/HPLPB20132509.2208
[10]	huang yonglin, ye yutang, qiao naosheng, chen zhenlong. Infrared image segmentation based on fast fuzzy C-means clustering[J]. High Power Laser and Particle Beams, 2011, 23(06): 0- .
[11]	li fan, liu shangqian, hong ming, qin hanlin. Algorithm of infrared image segmentation based on ant colony[J]. High Power Laser and Particle Beams, 2010, 22(05): 0- .
[12]	zhang libao, ao pengliang. Adaptive filtering algorithm for mixed noise image based on wavelet transform[J]. High Power Laser and Particle Beams, 2010, 22(11): 0- .
[13]	jing yuefeng, zhang xiaolin, guan yonghong. Radiography image segmentation model based on level set[J]. High Power Laser and Particle Beams, 2010, 22(01): 0- .
[14]	zhang libao, li dongling, yu xianchuan, wang pengfei, cai lei. Region-of-interest image edge detection based on histogram[J]. High Power Laser and Particle Beams, 2010, 22(08): 0- .
[15]	zhang xiaolin, jing yuefeng, liu jun. Edge detection method based on Facet model for flash X-ray radiographs[J]. High Power Laser and Particle Beams, 2010, 22(07): 0- .
[16]	chi fangting, li bo, liu yiyang, chen sufen, jiang bo. Ultraviolet light and ozone surface modification of poly-alpha α-methylstyrene using electroless nickel plating[J]. High Power Laser and Particle Beams, 2009, 21(03): 0- .
[17]	zhang xiaolin, jing yuefeng, liu jun. An improved edge detection method with Sobel operator in high-energy flash X-ray radiography[J]. High Power Laser and Particle Beams, 2009, 21(11): 0- .
[18]	liu chang-song, yan gao-shi, cai jian-rong. A new search algorithm for fast block-matching motion estimation[J]. High Power Laser and Particle Beams, 2007, 19(10): 0- .
[19]	yi heng-yu, ye yi-dong, lbaida, . Measurement of image segmentation based on optical interference[J]. High Power Laser and Particle Beams, 2004, 16(03): 0- .
[20]	yi heng yu, ye yi dong, zhang wei, ji yun song. Detection of targets under complicated background of cloud[J]. High Power Laser and Particle Beams, 2002, 14(05): 0- .