The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Geodesic active contours
Abstract
A novel scheme for the detection of object boundaries is presented. The technique is based on active contours deforming according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both interior and exterior boundaries. The proposed approach is based on the relation between active contours and the computation of geodesics or minimal distance curves. The minimal distance curve lays in a Riemannian space whose metric as defined by the image content. This geodesic approach for object segmentation allows to connect classical "snakes" based on energy minimization and geometric active contours based on the theory of curve evolution. Previous models of geometric active contours are improved as showed by a number of examples. Formal results concerning existence, uniqueness, stability, and correctness of the evolution are presented as well.>
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Cites
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,070
Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
Osher, S., & Sethian, J. A. (1988) · Journal of Computational Physics · doi:10.1016/0021-9991(88)90002-2 · Cited by 14,062
User’s guide to viscosity solutions of second order partial differential equations
Crandall, M. G., Ishii, H., & Lions, P.-L. (1992) · Bulletin of the American Mathematical Society · doi:10.1090/s0273-0979-1992-00266-5 · Cited by 4,781
Shape modeling with front propagation: a level set approach
Malladi, R., Sethian, J. A., & Vemuri, B. C. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.368173 · Cited by 3,158
The design and use of steerable filters
Freeman, W. T., & Adelson, E. H. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.93808 · Cited by 2,942
Image Selective Smoothing and Edge Detection by Nonlinear Diffusion. II
Alvarez, L., Lions, P.-L., & Morel, J.-M. (1992) · SIAM Journal on Numerical Analysis · doi:10.1137/0729052 · Cited by 2,208
On active contour models and balloons
Cohen, L. D. (1991) · CVGIP: Image Understanding · doi:10.1016/1049-9660(91)90028-n · Cited by 2,153
Blake, A., & Zisserman, A. (1987) · The MIT Press eBooks · doi:10.7551/mitpress/7132.001.0001 · Cited by 2,105
A geometric model for active contours in image processing
Caselles, V., Catt�, F., Coll, T., & Dibos, F. (1993) · Numerische Mathematik · doi:10.1007/bf01385685 · Cited by 1,743
Self-citationIntroduction to applied mathematics.
(1986) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(86)90036-4 · Cited by 1,495
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GrabCut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015720 · Cited by 5,703
Yilmaz, A., Javed, O., & Shah, M. (2006) · ACM Computing Surveys · doi:10.1145/1177352.1177355 · Cited by 4,792
Snakes, shapes, and gradient vector flow
Xu, C., & Prince, J. L. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.661186 · Cited by 4,001
Deformable models in medical image analysis: a survey
McInerney, T., & Terzopoulos, D. (1996) · Medical Image Analysis · doi:10.1016/s1361-8415(96)80007-7 · Cited by 1,913
Active contours driven by local image fitting energy
Zhang, K., Song, H., & Zhang, L. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2009.10.010 · Cited by 721
Metadata
- Authors
- Caselles, V., Kimmel, R., & Sapiro, G.
- Venue
- Proceedings of IEEE International Conference on Computer Vision
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-11-19
- Pages
- 694-699
- OpenAlex
- W2134820502
- Cited by
- 1,161
- References
- 31
- FWCI
- 39.41
- Type
- conference paper
- Language
- en
- Open access
- closed
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