TY - GEN
T1 - An active contour model without edges
AU - Chan, Tony
AU - Vese, Luminita
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1999.
PY - 1999
Y1 - 1999
N2 - In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. The model is a combination between more classical active contour models using mean curvature motion techniques, and the Mumford-Shah model for segmentation. We minimize an energy which can be seen as a particular case of the so-called minimal partition problem. In the level set formulation, the problem becomes a \mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. Finally, we will present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable.
AB - In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. The model is a combination between more classical active contour models using mean curvature motion techniques, and the Mumford-Shah model for segmentation. We minimize an energy which can be seen as a particular case of the so-called minimal partition problem. In the level set formulation, the problem becomes a \mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. Finally, we will present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable.
UR - http://www.scopus.com/inward/record.url?scp=84948181037&partnerID=8YFLogxK
U2 - 10.1007/3-540-48236-9_13
DO - 10.1007/3-540-48236-9_13
M3 - Conference contribution
AN - SCOPUS:84948181037
SN - 354066498X
SN - 9783540664987
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 141
EP - 151
BT - Scale-Space Theories in Computer Vision - 2nd International Conference, Scale-Space 1999, Proceedings
A2 - Nielsen, Mads
A2 - Johansen, Peter
A2 - Olsen, Ole Fogh
A2 - Weickert, Joachim
PB - Springer Verlag
T2 - 2nd International Conference on Scale-Space Theories in Computer Vision, 1999
Y2 - 26 September 1999 through 27 September 1999
ER -