Abstract
Active contour and surface models, also known as deformable models, constitute a class of powerful segmentation techniques. Geometric deformable models implemented via level-set methods have advantages over parametric ones due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models-the ability to automatically handle topology changes-turns out to be a liability in applications where the objects to be segmented have a known topology that must be preserved. In this paper, we present a geometric deformable model that preserves topology using the simple point concept from digital topology This algorithm maintains the other advantages of standard geometric deformable models including sub-pixel accuracy and production of nonintersecting curves (or surfaces). Several experiments on simulated and real data are provided to demonstrate the performance of the proposed algorithm.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| Volume | 2 |
| State | Published - 2001 |
| Event | 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Kauai, HI, United States Duration: Dec 8 2001 → Dec 14 2001 |
Other
| Other | 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
|---|---|
| Country | United States |
| City | Kauai, HI |
| Period | 12/8/01 → 12/14/01 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Software
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
A topology preserving deformable model using level sets. / Han, Xiao; Xu, Chenyang; Prince, Jerry Ladd.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2 2001.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A topology preserving deformable model using level sets
AU - Han, Xiao
AU - Xu, Chenyang
AU - Prince, Jerry Ladd
PY - 2001
Y1 - 2001
N2 - Active contour and surface models, also known as deformable models, constitute a class of powerful segmentation techniques. Geometric deformable models implemented via level-set methods have advantages over parametric ones due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models-the ability to automatically handle topology changes-turns out to be a liability in applications where the objects to be segmented have a known topology that must be preserved. In this paper, we present a geometric deformable model that preserves topology using the simple point concept from digital topology This algorithm maintains the other advantages of standard geometric deformable models including sub-pixel accuracy and production of nonintersecting curves (or surfaces). Several experiments on simulated and real data are provided to demonstrate the performance of the proposed algorithm.
AB - Active contour and surface models, also known as deformable models, constitute a class of powerful segmentation techniques. Geometric deformable models implemented via level-set methods have advantages over parametric ones due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models-the ability to automatically handle topology changes-turns out to be a liability in applications where the objects to be segmented have a known topology that must be preserved. In this paper, we present a geometric deformable model that preserves topology using the simple point concept from digital topology This algorithm maintains the other advantages of standard geometric deformable models including sub-pixel accuracy and production of nonintersecting curves (or surfaces). Several experiments on simulated and real data are provided to demonstrate the performance of the proposed algorithm.
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UR - http://www.scopus.com/inward/citedby.url?scp=0035696551&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0035696551
VL - 2
BT - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ER -