TY - GEN
T1 - One-shot integral invariant shape priors for variational segmentation
AU - Manay, Siddharth
AU - Cremers, Daniel
AU - Yezzi, Anthony
AU - Soatto, Stefano
PY - 2005
Y1 - 2005
N2 - We match shapes, even under severe deformations, via a smooth reparametrization of their integral invariant signatures. These robust signatures and correspondences are the foundation of a shape energy functional for variational image segmentation. Integral invariant shape templates do not require registration and allow for significant deformations of the contour, such as the articulation of the object's parts. This enables generalization to multiple instances of a shape from a single template, instead of requiring several templates for searching or training. This paper motivates and presents the energy functional, derives the gradient descent direction to optimize the functional, and demonstrates the method, coupled with a data term, on real image data where the object's parts are articulated.
AB - We match shapes, even under severe deformations, via a smooth reparametrization of their integral invariant signatures. These robust signatures and correspondences are the foundation of a shape energy functional for variational image segmentation. Integral invariant shape templates do not require registration and allow for significant deformations of the contour, such as the articulation of the object's parts. This enables generalization to multiple instances of a shape from a single template, instead of requiring several templates for searching or training. This paper motivates and presents the energy functional, derives the gradient descent direction to optimize the functional, and demonstrates the method, coupled with a data term, on real image data where the object's parts are articulated.
UR - http://www.scopus.com/inward/record.url?scp=33646545698&partnerID=8YFLogxK
U2 - 10.1007/11585978_27
DO - 10.1007/11585978_27
M3 - Conference contribution
AN - SCOPUS:33646545698
SN - 3540302875
SN - 9783540302872
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 414
EP - 426
BT - Energy Minimization Methods in Computer Vision and Pattern Recognition - 5th International Workshop, EMMCVPR 2005, Proceedings
T2 - 5th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2005
Y2 - 9 November 2005 through 11 November 2005
ER -