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New possibilities with Sobolev active contours

Publication ,  Journal Article
Sundaramoorthi, G; Yezzi, A; Mennucci, AC; Sapiro, G
Published in: International Journal of Computer Vision
August 1, 2009

Recently, the Sobolev metric was introduced to define gradient flows of various geometric active contour energies. It was shown that the Sobolev metric outperforms the traditional metric for the same energy in many cases such as for tracking where the coarse scale changes of the contour are important. Some interesting properties of Sobolev gradient flows include that they stabilize certain unstable traditional flows, and the order of the evolution PDEs are reduced when compared with traditional gradient flows of the same energies. In this paper, we explore new possibilities for active contours made possible by Sobolev metrics. The Sobolev method allows one to implement new energy-based active contour models that were not otherwise considered because the traditional minimizing method render them ill-posed or numerically infeasible. In particular, we exploit the stabilizing and the order reducing properties of Sobolev gradients to implement the gradient descent of these new energies. We give examples of this class of energies, which include some simple geometric priors and new edge-based energies. We also show that these energies can be quite useful for segmentation and tracking. We also show that the gradient flows using the traditional metric are either ill-posed or numerically difficult to implement, and then show that the flows can be implemented in a stable and numerically feasible manner using the Sobolev gradient. © 2008 Springer Science+Business Media, LLC.

Duke Scholars

Published In

International Journal of Computer Vision

DOI

EISSN

1573-1405

ISSN

0920-5691

Publication Date

August 1, 2009

Volume

84

Issue

2

Start / End Page

113 / 129

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4607 Graphics, augmented reality and games
  • 4603 Computer vision and multimedia computation
  • 0801 Artificial Intelligence and Image Processing
 

Citation

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Sundaramoorthi, G., Yezzi, A., Mennucci, A. C., & Sapiro, G. (2009). New possibilities with Sobolev active contours. International Journal of Computer Vision, 84(2), 113–129. https://doi.org/10.1007/s11263-008-0133-9
Sundaramoorthi, G., A. Yezzi, A. C. Mennucci, and G. Sapiro. “New possibilities with Sobolev active contours.” International Journal of Computer Vision 84, no. 2 (August 1, 2009): 113–29. https://doi.org/10.1007/s11263-008-0133-9.
Sundaramoorthi G, Yezzi A, Mennucci AC, Sapiro G. New possibilities with Sobolev active contours. International Journal of Computer Vision. 2009 Aug 1;84(2):113–29.
Sundaramoorthi, G., et al. “New possibilities with Sobolev active contours.” International Journal of Computer Vision, vol. 84, no. 2, Aug. 2009, pp. 113–29. Scopus, doi:10.1007/s11263-008-0133-9.
Sundaramoorthi G, Yezzi A, Mennucci AC, Sapiro G. New possibilities with Sobolev active contours. International Journal of Computer Vision. 2009 Aug 1;84(2):113–129.
Journal cover image

Published In

International Journal of Computer Vision

DOI

EISSN

1573-1405

ISSN

0920-5691

Publication Date

August 1, 2009

Volume

84

Issue

2

Start / End Page

113 / 129

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4607 Graphics, augmented reality and games
  • 4603 Computer vision and multimedia computation
  • 0801 Artificial Intelligence and Image Processing