@Article{CiCP-26-206,
author = {Po-Wen Hsieh, Pei-Chiang Shao and Suh-Yuh Yang},
title = {Advection-Enhanced Gradient Vector Flow for Active-Contour Image Segmentation},
journal = {Communications in Computational Physics},
year = {2019},
volume = {26},
number = {1},
pages = {206--232},
abstract = {<p style="text-align: justify;">In this paper, we propose a new gradient vector flow model with advection
enhancement, called advection-enhanced gradient vector flow, for calculating the external force employed in the active-contour image segmentation. The proposed model
is mainly inspired by the functional derivative of an adaptive total variation regularizer whose minimizer is expected to be able to effectively preserve the desired object
boundary. More specifically, by incorporating an additional advection term into the
usual gradient vector flow model, the resulting external force can much better help the
active contour to recover missing edges, to converge to a narrow and deep concavity,
and to preserve weak edges. Numerical experiments are performed to demonstrate
the high performance of the newly proposed model.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2018-0068},
url = {http://global-sci.org/intro/article_detail/cicp/13032.html}
}