Full Text:   <1695>

CLC number: TP391.7; TP317.4

On-line Access: 2012-06-05

Received: 2011-11-15

Revision Accepted: 2012-03-23

Crosschecked: 2012-05-09

Cited: 1

Clicked: 2865

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.6 P.428-439


Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method

Author(s):  Shi-yan Wang, Hui-min Yu

Affiliation(s):  Department of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   wangshiyan@zju.edu.cn, yhm2005@zju.edu.cn

Key Words:  3D spatiotemporal segmentation, Motion estimation, Total variation, Primal-dual

Shi-yan Wang, Hui-min Yu. Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method[J]. Journal of Zhejiang University Science C, 2012, 13(6): 428-439.

@article{title="Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method",
author="Shi-yan Wang, Hui-min Yu",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method
%A Shi-yan Wang
%A Hui-min Yu
%J Journal of Zhejiang University SCIENCE C
%V 13
%N 6
%P 428-439
%@ 1869-1951
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100331

T1 - Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method
A1 - Shi-yan Wang
A1 - Hui-min Yu
J0 - Journal of Zhejiang University Science C
VL - 13
IS - 6
SP - 428
EP - 439
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1100331

A method based on 3D videos is proposed for multi-target segmentation and tracking with a moving viewing system. A spatiotemporal energy functional is built up to perform motion segmentation and estimation simultaneously. To overcome the limitation of the local minimum problem with the level set method, a convex relaxation method is applied to the 3D spatiotemporal segmentation model. The relaxed convex model is independent of the initial condition. A primal-dual algorithm is used to improve computational efficiency. Several indoor experiments show the validity of the proposed method.

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


[1]Alvarez, L., Castano, C.A., Garcia, M., Krissian, K., Mazorra, L., Salgado, A., Sanchez, J., 2009. A new energy-based method for 3D motion estimation of incompressible PIV flows. Comput. Vis. Image Understand., 113(7):802-810.

[2]Boykov, Y., Funka-Lea, G., 2006. Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis., 70(2):109-131.

[3]Boykov, Y., Kolmogorov, V., 2004. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell., 26(9):1124-1137.

[4]Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J., Osher, S., 2007. Fast global minimization of the active contour/snake model. J. Math. Imag. Vis., 28(2):151-167.

[5]Caselles, V., Kimmell, R., Sapiro, G., 1997. Geodesic active contours. Int. J. Comput. Vis., 22(1):61-79.

[6]Chambolle, A., 2004. An algorithm for total variation minimization and applications. J. Math. Imag. Vis., 20(1-2):89-97.

[7]Chan, T.F., Vese, L.A., 2001. Active contours without edges. IEEE Trans. Image Process., 10(2):266-277.

[8]Chan, T.F., Golub, G.H., Mulet, P., 1999. A nonlinear primal-dual method for total variation-based image restoration. SIAM J. Sci. Comput., 20(6):1964-1977.

[9]Chan, T.F., Esedoglu, S., Nikolova, M., 2004. Algorithms for finding global minimizers of image segmentation and denoising models. SIAM J. Appl. Math., 66:1632-1648.

[10]Dufaux, F., Moccagatta, I., Moscheni, F., Nicolas, H., 1994. Vector quantization-based motion field segmentation under the entropy criterion. J. Vis. Commun. Image Represent., 5:356-369.

[11]Feghali, R., Mitiche, A., 2004. Spatiotemporal motion boundary detection and motion boundary velocity estimation for tracking moving objects with a moving camera: a level sets PDEs approach with concurrent camera motion compensation. IEEE Trans. Image Process., 13(11):1473-1490.

[12]Goldstein, T., Bresson, X., Osher, S., 2009. Geometric applications of the split Bregman method: segmentation and surface reconstruction. J. Sci. Comput., 45(1-3):272-293.

[13]Heeger, D.J., Jepson, A.D., 1992. Subspace methods for recovering rigid motion I: algorithm and implementation. Int. J. Comput. Vis., 7(2):95-117.

[14]Horn, B.K.P., Schunck, B.G., 1981. Determining optical flow. Artif. Intell., 17(1-3):185-203.

[15]Jonasson, L., Bresson, X., Hagmann, P., Cuisenaire, O., Meuli, R., Thiran, J.P., 2005. White matter fiber tract segmentation in DT-MRI using geometric flows. Med. Image Anal., 9(3):223-236.

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

[17]Leventon, M.E., Grimson, W.E.L., Faugeras, O., 2000. Statistical Shape Influence in Geodesic Active Contours. IEEE Conf. on Computer Vision and Pattern Recognition, 1:316-323.

[18]Longuet-Higgins, H.C., Prazdny, K., 1980. The interpretation of a moving retinal image. Proc. R. Soc. Lond. B, 208(1173):385-397.

[19]Malladi, R., Kimmel, R., Adalsteinsson, D., Sapiro, G., Caselles, V., Sethian, J.A., 1996. A Geometric Approach to Segmentation and Analysis of 3D Medical Images. Proc. Workshop on Mathematical Methods in Biomedical Image Analysis, p.244-252.

[20]Merriman, B., Bence, J.K., Osher, S.J., 1994. Motion of multiple junctions: a level set approach. J. Comput. Phys., 112(2):334-363.

[21]Mémin, E., Pérez, P., 2002. Hierarchical estimation and segmentation of dense motion fields. Int. J. Comput. Vis., 46(2):129-155.

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

[23]Ohno, K., Nomura, T., Tadokoro, S., 2006. Real-Time Robot Trajectory Estimation and 3D Map Construction Using 3D Camera. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.5279-5285.

[24]Osher, S., Sethian, J., 1988. Fronts propagating with curvature dependent speed: algorithms based on the Hamilton-Jacobi formulation. J. Comput. Phys., 79(1):12-49.

[25]Paragios, N., Deriche, R., 2005. Geodesic active regions and level set methods for motion estimation and tracking. Comput. Vis. Image Understand., 97(3):259-282.

[26]Rudin, L.I., Osher, S., Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Phys. D, 60(1-4):259-268.

[27]Schnorr, C., 1991. Determining optical flow for irregular domains by minimizing quadratic functionals of a certain class. Int. J. Comput. Vis., 6(1):25-38.

[28]Wang, S., Yu, H., 2011. A Variational Approach for Ego-motion Estimation and Segmentation Based on 3D TOF Camera. 4th Int. Congress on Image and Signal Processing, p.1160-1164.

[29]Ye, C., Hegde, G.P.M., 2009. Robust Edge Extraction for SwissRange SR-3000 Range Image. Proc. IEEE Int. Conf. on Robotics and Automation, p.2437-2442.

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 - Journal of Zhejiang University-SCIENCE