Full Text:   <3096>

CLC number: TP391

On-line Access: 

Received: 2005-05-18

Revision Accepted: 2005-12-19

Crosschecked: 0000-00-00

Cited: 0

Clicked: 6174

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.2 P.109-116


Jacquard image segmentation using Mumford-Shah model

Author(s):  Feng Zhi-lin, Yin Jian-wei, Chen Gang, Dong Jin-xiang

Affiliation(s):  Department of Information and Engineering, College of Zhijiang, Zhejiang University of Technology, Hangzhou 310024, China; more

Corresponding email(s):   fzlmailbox@21cn.com

Key Words:  Mumford-Shah model, Image segmentation, Active contour, Variational method, Jacquard image

Share this article to: More |Next Article >>>

Feng Zhi-lin, Yin Jian-wei, Chen Gang, Dong Jin-xiang. Jacquard image segmentation using Mumford-Shah model[J]. Journal of Zhejiang University Science A, 2006, 7(2): 109-116.

@article{title="Jacquard image segmentation using Mumford-Shah model",
author="Feng Zhi-lin, Yin Jian-wei, Chen Gang, Dong Jin-xiang",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Jacquard image segmentation using Mumford-Shah model
%A Feng Zhi-lin
%A Yin Jian-wei
%A Chen Gang
%A Dong Jin-xiang
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 2
%P 109-116
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0109

T1 - Jacquard image segmentation using Mumford-Shah model
A1 - Feng Zhi-lin
A1 - Yin Jian-wei
A1 - Chen Gang
A1 - Dong Jin-xiang
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 2
SP - 109
EP - 116
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A0109

Jacquard image segmentation is one of the primary steps in image analysis for jacquard pattern identification. The main aim is to recognize homogeneous regions within a jacquard image as distinct, which belongs to different patterns. active contour models have become popular for finding the contours of a pattern with a complex shape. However, the performance of active contour models is often inadequate under noisy environment. In this paper, a robust algorithm based on the mumford-Shah model is proposed for the segmentation of noisy jacquard images. First, the mumford-Shah model is discretized on piecewise linear finite element spaces to yield greater stability. Then, an iterative relaxation algorithm for numerically solving the discrete version of the model is presented. In this algorithm, an adaptive triangular mesh is refined to generate Delaunay type triangular mesh defined on structured triangulations, and then a quasi-Newton numerical method is applied to find the absolute minimum of the discrete model. Experimental results on noisy jacquard images demonstrated the efficacy of the proposed algorithm.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1] Ambrosio, L., Tortorelli, V.M., 1990. Approximation of functionals depending on jumps by elliptic functionals via Gamma-convergence. Comm. Pure Appl. Math., 43:999-1036.

[2] Borouchaki, H., Laug, P., 1996. The BL2D Mesh Generator: Beginner’s Guide, User’s and Programmer’s Manual. Technical Report RT-0194, INRIA.

[3] Chambolle, A., 1995. Image segmentation by variational methods: Mumford and Shah functional and the discrete approximations. SIAM J. Appl. Math., 55(3):827-863.

[4] Chen, X.F., Guan, Z.C., 2004. Image segmentation Based on Mumford-Shah functional. Journal of Zhejiang University SCIENCE, 5(1):123-128.

[5] Chesnaud, C., Refregier, P., Boulet, V., 1999. Statistical region snake-based segmentation Adapted to different physical noise models. IEEE Trans. Pattern Analysis and Machine Intelligence, 21(11):1145-1157.

[6] dal Maso, G., 1993. An Introduction to Γ-Convergence. Birkhäuse, Boston.

[7] de Giorgi, E., Carriero, M., Leaci, A., 1990. Existence theorem for a minimum problem with free discontinuity set. Arch. Rational Mech. Anal., 111(4):291-322.

[8] George, P.L., Borouchaki, H., 1998. Delaunay Triangulation and Meshing: Application to Finite Elements. Hermes, Paris.

[9] Gobbino, M., 1998. Finite difference approximation of the Mumford-Shah functional. Comm. Pure Appl. Math., 51(2):197-228.

[10] Kass, M., Witkin, A., Terzopoulos, D., 1988. Snakes: Active contour models. Int. J. Computer Vis., 1(4):321-331.

[11] Malladi, R., Sethian, J.A., Vemuri, B.C., 1995. Shape modeling with front propagation: A level set approach. IEEE Trans. Pattern Analysis and Machine Intelligence, 17(2):158-175.

[12] Martin, P., Goudail, F., Refregier, P., Guerault, F., 2004. Influence of the noise model on level set active contour segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence, 26(6):799-803.

[13] Mumford, D., Shah, J., 1989. Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math., 42(5):577-685.

[14] Negri, M., 1999. The anisotropy introduced by the mesh in the finite element approximation of the Mumford-Shah Functional. Numer. Funct. Anal. Optim., 20:957-982.

[15] Tsai, A., Yezzi, A.Jr., Willsky, A.S., 2001. Curve Evolution implementation of the Mumford-Shah Functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. Image Processing, 10(8):1169-1186.

[16] Xiao, H., Xu, C.Y., Prince, J.L., 2003. A topology preserving level set method for geometric deformable models. IEEE Trans. Pattern Analysis and Machine Intelligence, 25(6):755-768.

[17] Zhuang, X.H., Huang, Y., Palaniappan, K., Zhao, Y.X., 1996. Gaussian mixture density modeling, decomposition, and applications. IEEE Trans. Image Processing, 5(9):1293-1302.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE