TY - GEN
T1 - Geodesic active contours with combined shape and appearance priors
AU - Ben-Ari, Rami
AU - Aiger, Dror
PY - 2008/1/1
Y1 - 2008/1/1
N2 - We present a new object segmentation method that is based on geodesic active contours with combined shape and appearance priors. It is known that using shape priors can significantly improve object segmentation in cluttered scenes and occlusions. Within this context, we add a new prior, based on the appearance of the object, (i.e., an image) to be segmented. This method enables the appearance pattern to be incorporated within the geodesic active contour framework with shape priors, seeking for the object whose boundaries lie on high image gradients and that best fits the shape and appearance of a reference model. The output contour results from minimizing an energy functional built of these three main terms. We show that appearance is a powerful term that distinguishes between objects with similar shapes and capable of successfully segment an object in a very cluttered environment where standard active contours (even those with shape priors) tend to fail.
AB - We present a new object segmentation method that is based on geodesic active contours with combined shape and appearance priors. It is known that using shape priors can significantly improve object segmentation in cluttered scenes and occlusions. Within this context, we add a new prior, based on the appearance of the object, (i.e., an image) to be segmented. This method enables the appearance pattern to be incorporated within the geodesic active contour framework with shape priors, seeking for the object whose boundaries lie on high image gradients and that best fits the shape and appearance of a reference model. The output contour results from minimizing an energy functional built of these three main terms. We show that appearance is a powerful term that distinguishes between objects with similar shapes and capable of successfully segment an object in a very cluttered environment where standard active contours (even those with shape priors) tend to fail.
UR - https://www.scopus.com/pages/publications/57049141523
U2 - 10.1007/978-3-540-88458-3_45
DO - 10.1007/978-3-540-88458-3_45
M3 - Conference contribution
AN - SCOPUS:57049141523
SN - 3540884572
SN - 9783540884576
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 494
EP - 505
BT - Advanced Concepts for Intelligent Vision Systems - 10th International Conference, ACIVS 2008, Proceedings
PB - Springer Verlag
T2 - 10th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2008
Y2 - 20 October 2008 through 24 October 2008
ER -