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New possibilities with Sobolev active contours

Publication ,  Journal Article
Sundaramoorthi, G; Yezzi, A; Mennucci, AC; Sapiro, G
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2007

Recently, the Sobolev metric was introduced to define gradient flows of various geometric active contour energies. It was shown that the Sobolev metric out-performs 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 are 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 active contours. The Sobolev method allows one to implement new energy-based active contour models that were not otherwise considered because the traditional minimizing method cannot be used. 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 will show that these energies can be quite useful for segmentation and tracking. We will 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. © Springer-Verlag Berlin Heidelberg 2007.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2007

Volume

4485 LNCS

Start / End Page

153 / 164

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Sundaramoorthi, G., Yezzi, A., Mennucci, A. C., & Sapiro, G. (2007). New possibilities with Sobolev active contours. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4485 LNCS, 153–164. https://doi.org/10.1007/978-3-540-72823-8_14
Sundaramoorthi, G., A. Yezzi, A. C. Mennucci, and G. Sapiro. “New possibilities with Sobolev active contours.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4485 LNCS (January 1, 2007): 153–64. https://doi.org/10.1007/978-3-540-72823-8_14.
Sundaramoorthi G, Yezzi A, Mennucci AC, Sapiro G. New possibilities with Sobolev active contours. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2007 Jan 1;4485 LNCS:153–64.
Sundaramoorthi, G., et al. “New possibilities with Sobolev active contours.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4485 LNCS, Jan. 2007, pp. 153–64. Scopus, doi:10.1007/978-3-540-72823-8_14.
Sundaramoorthi G, Yezzi A, Mennucci AC, Sapiro G. New possibilities with Sobolev active contours. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2007 Jan 1;4485 LNCS:153–164.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2007

Volume

4485 LNCS

Start / End Page

153 / 164

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences