1997
@article{CKS1997,
vgclass = {refpap},
author = {Vicent Caselles and Ron Kimmel and Guillermo Sapiro},
title = {Geodesic Active Contours},
journal = {International Journal of Computer Vision},
volume = {22},
number = {1},
pages = {61--79},
month = {February},
year = {1997},
abstract = {A novel scheme for the detection of object boundaries is
presented. The technique is based on active contours evolving in time
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 is 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, allowing
stable boundary detection when their gradients suffer from large
variations, including gaps. Formal results concerning existence,
uniqueness, stability, and correctness of the evolution are presented
as well. The scheme was implemented using an efficient algorithm for
curve evolution. Experimental results of applying the scheme to real
images including objects with holes and medical data imagery
demonstrate its power. The results may be extended to 3D object
segmentation as well.},
}