@Article{L.Garrido2015,
author="L.Garrido
and M.Guerrieri
and Laura Igual",
title="Image Segmentation with Cage Active Contours",
journal="IEEE Transactions on Image Processing",
year="2015",
volume="24",
number="12",
pages="5557--5566",
optkeywords="Level sets",
optkeywords="Mean value coordinates",
optkeywords="Parametrized active contours",
abstract="In this paper, we present a framework for image segmentation based on parametrized active contours. The evolving contour is parametrized according to a reduced set of control points that form a closed polygon and have a clear visual interpretation. The parametrization, called mean value coordinates, stems from the techniques used in computer graphics to animate virtual models. Our framework allows to easily formulate region-based energies to segment an image. In particular, we present three different local region-based energy terms: 1) the mean model; 2) the Gaussian model; 3) and the histogram model. We show the behavior of our method on synthetic and real images and compare the performance with state-of-the-art level set methods.",
optnote="MILAB",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2673), last updated on Wed, 14 Oct 2015 10:54:59 +0200",
issn="1057-7149",
doi="10.1109/TIP.2015.2472298"
}