Skip to main content
Log in

Sobolev Active Contours

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

All previous geometric active contour models that have been formulated as gradient flows of various energies use the same L 2-type inner product to define the notion of gradient. Recent work has shown that this inner product induces a pathological Riemannian metric on the space of smooth curves. However, there are also undesirable features associated with the gradient flows that this inner product induces. In this paper, we reformulate the generic geometric active contour model by redefining the notion of gradient in accordance with Sobolev-type inner products. We call the resulting flows Sobolev active contours. Sobolev metrics induce favorable regularity properties in their gradient flows. In addition, Sobolev active contours favor global translations, but are not restricted to such motions; they are also less susceptible to certain types of local minima in contrast to traditional active contours. These properties are particularly useful in tracking applications. We demonstrate the general methodology by reformulating some standard edge-based and region-based active contour models as Sobolev active contours and show the substantial improvements gained in segmentation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Adalsteinsson, D. and Sethian, J. 1995. A fast level set method for propagating interfaces. J. Comp. Phys., 118:269–277.

    Article  MathSciNet  MATH  Google Scholar 

  • Blake, A. and Isard, M. 1998, Active Contours. Springer Verlag.

  • Burger, M. 2003. A framework for the construction of level set methods for shape optimization and reconstruction. Interfaces Free Boundaries, 5:301–329.

    MathSciNet  MATH  Google Scholar 

  • Burger, M. and Osher, S. 2005. A survey on level set methods for inverse problems and optimal design. Eur. J. Appl. Math., 16.

  • Caselles, V., Catte, F., Coll, T., and Dibos, F. 1993. A geometric model for edge detection. Num. Mathematik, 66:1–31.

    Article  MathSciNet  MATH  Google Scholar 

  • Caselles, V., Kimmel, R., and Sapiro, G. 1995. Geodesic active contours. In: Proc. of the IEEE Int. Conf. on Computer Vision, Cambridge, MA, USA, pp. 694–699.

  • Chan, T. and Vese, L. 2001. Active contours without edges. IEEE Transactions on Image Processing, 10(2):266–277.

    Article  MATH  Google Scholar 

  • Charpiat, G., Faugeras, O.D., and Keriven, R. 2005a. Approximations of shape metrics and application to shape warping and empirical shape statistics. Foundations Comput. Math., 5(1):1–58.

    Article  MathSciNet  MATH  Google Scholar 

  • Charpiat, G., Keriven, R., Pons, J., and Faugeras, O. 2005b. Designing spatially coherent minimizing flows for variational problems base d on Active Contours. In: ICCV.

  • Chen, Y., Tagare, H., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K., Briggs, R., and Geiser, E. 2002. Using prior shapes in geometric active contours in a variational framework. Int. J. Comput. Vision, 50(3):315–328.

    Article  MATH  Google Scholar 

  • Chopp, D.L. and Sethian, J.A. 1999. Motion by intrinsic laplacian of curvature. Interfaces Free Boundaries, 1:107–123.

    MathSciNet  MATH  Google Scholar 

  • Cohen, L.D. 1991. On active contour models and ballons. Comput. Vision, Graphics, and Image Processing: Image Processing, 53(2).

  • Cremers, D. and Soatto, S. 2003. A pseuso distance for shape priors in level set segmentation. In: IEEE Int. Workshop on Variational, Geometric and Level Set Methods, pp. 169–176.

  • do Carmo, M. 1992. Riemannian Geometry. Birkhäuser Boston.

    MATH  Google Scholar 

  • Droske, M. and Rumpf, M. 2004. A level set formulation for the willmore flow. Interfaces and Boundaries, 6(3):361–378.

    Article  MathSciNet  MATH  Google Scholar 

  • Hintermüller, M. and Ring, W. 2004. An inexact Newton-CG-type active contour approach for the minimization of the mumford-shah functional. J. Math. Imaging Vision, 20(1):19–42.

    Article  MathSciNet  Google Scholar 

  • Kass, M., Witkin, A., and Terzopoulos, D. 1987. Snakes: Active contour models. Int. J. Comput. Vision, 1:321–331.

    Article  Google Scholar 

  • Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., and Yezzi, A. 1995. Gradient flows and geometric active contour models. In: Proc. of the IEEE Int. Conf. on Comput. Vision, pp. 810–815.

  • Lang, S. 1999. Fundamentals of Differential Geometry. Springer-Verlag.

  • Leventon, M., Grimson, E., and Faugeras, O. 2000. Statistical Shape influence in geodesic active contours. In: IEEE Conf. on Comp. Vision and Patt. Recog., vol. 1, pp. 316–323.

  • Malladi, R., Sethian, J., and Vemuri, B. 1995. Shape modeling with front propagation: a level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17:158–175.

    Article  Google Scholar 

  • Mansouri, A.-R., Mukherjee, D.P., and Acton, S.T. 2004. Constraining active contour evolution via Lie Groups of transformation. IEEE Transactions on Image Processing, 13(6):853–863.

    Article  MathSciNet  Google Scholar 

  • Mennucci, A.C.G., Yezzi, A., and Sundaramoorthi, G. 2006. Sobolev–type metrics in the space of curves. Preprint, arXiv:math.DG/0605017.

  • Michor, P. and Mumford, D. 2003. Riemannian geometries on the space of plane curves. ESI Preprint 1425, arXiv:math.DG/0312384.

  • Mio, W. and Srivastava, A. 2004. Elastic-string models for representation and analysis of planar shapes. In: CVPR, vol. 2, pp. 10–15.

  • Mumford, D. and Shah, J. 1985. Boundary detection by minimizing functionals. In: Proc. IEEE Conf. Computer Vision Pattern Recognition.

  • Mumford, D. and Shah, J. 1989. Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math., 42:577–685.

    MathSciNet  MATH  Google Scholar 

  • Neuberger, J.W. 1997. Sobolev Gradients and Differential Equations. Lecture Notes in Mathematics #1670. Springer.

  • Osher, S. and Sethian, J. 1988. Fronts propagating with curvature-dependent speed: algorithms based on the Hamilton-Jacobi equations. J. Comp. Phys., 79:12–49.

    Article  MathSciNet  MATH  Google Scholar 

  • Paragios, N. and Deriche, R. 2002a. Geodesic active regions: A new paradigm to deal with frame partition problems in computer vision. International Journal of Visual Communication and Image Representation, Special Issue on Partial Differential Equations in Image Processing, Computer Vision and Computer Graphics, 13(2):249–268.

    Google Scholar 

  • Paragios, N. and Deriche, R. 2002b. Geodesic active regions and level set methods for supervised texture segmentation. Int. J. Comput. Vision, 46(3):223.

    Article  MATH  Google Scholar 

  • Raviv, T.R., Kiryati, N., and Sochen, N. 2004. Unlevel-set: Geometry and prior-based segmentation. In: Proc. European Conf. on Computer Vision.

  • Ronfard, R. 1994. Region based strategies for active contour models. Int. J. Comput. Vision, 13(2):229–251.

    Article  Google Scholar 

  • Rousson, M. and Paragios, N. 2002. Shape Priors for Level Set Representations. In: Proc. European Conf. Computer Vision, vol. 2, pp. 78–93.

  • Rouy, E. and Tourin, A. 1992. A viscosity solutions approach to shape-from-shading. SIAM J. Numerical Anal., 29(3):867–884.

    Article  MathSciNet  MATH  Google Scholar 

  • Siddiqi, K., Lauzière, Y.B., Tannenbaum, A., and Zucker, S. 1998. Area and length minimizing flows for shape segmentation. IEEE Transactions on Image Processing, 3(7):433–443.

    Article  Google Scholar 

  • Soatto, S. and Yezzi, A.J. 2002. DEFORMOTION: Deforming motion, shape average and the joint registration and segmentation of images. In: ECCV, vol. 3, pp. 32–57.

  • Sundaramoorthi, G., Yezzi, A., and Mennucci, A. 2005. Sobolev active contours. In: VLSM, pp. 109–120.

  • Tsai, A., Yezzi, A., and Willsky, A.S. 2001a. Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Transactions on Image Processing, 10(8):1169–1186.

    Article  MATH  Google Scholar 

  • Tsai, A., Yezzi, A.J., III, W.M.W., Tempany, C., Tucker, D., Fan, A., Grimson, W.E.L., and Willsky, A.S. 2001b. Model-based curve evolution technique for image segmentation. In: CVPR, vol. 1, pp. 463–468.

  • Vese, L.A. and Chan, T.F. 2002. A multiphase level set framework for image segmentation using the mumford and shah model. Int. J. Comput. Vision, 50(3):271–293.

    Article  MATH  Google Scholar 

  • Xu, C. and Prince, J.L. 1998. Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing, 7(3):359–369.

    Article  MathSciNet  MATH  Google Scholar 

  • Yezzi, A. and Mennucci, A. 2005a. Metrics in the space of curves. Preprint, arXiv:math.DG /0412454.

  • Yezzi, A., Tsai, A., and Willsky, A. 1999. A statistical approach to snakes for bimodal and trimodal imagery. In: Int. Conf. on Comput. Vision, pp. 898–903.

  • Yezzi, A.J. and Mennucci, A. 2005b. Conformal metrics and true “Gradient flows” for curves. In: ICCV, pp. 913–919.

  • Younes, L. 1998. Computable elastic distances between shapes. SIAM J. Appl. Math., 58(2):565–586.

    Article  MathSciNet  MATH  Google Scholar 

  • Zhu, S.C., Lee, T.S., and Yuille, A.L. 1995. Region competition: Unifying snakes, region growing, Energy/Bayes/MDL for multi-band image segmentation. In: ICCV, pp. 416–423.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ganesh Sundaramoorthi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sundaramoorthi, G., Yezzi, A. & Mennucci, A.C. Sobolev Active Contours. Int J Comput Vision 73, 345–366 (2007). https://doi.org/10.1007/s11263-006-0635-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11263-006-0635-2

Keywords

Navigation