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@article{948670,
author = {Daněk, Ondřej and Matula, Pavel and Maška, Martin and Kozubek, Michal},
article_location = {Amsterdam},
article_number = {10},
doi = {http://dx.doi.org/10.1016/j.patrec.2012.03.013},
keywords = {image segmentation; graph cut framework; Chan-Vese model; boundary smoothness; memory consumption},
language = {eng},
issn = {0167-8655},
journal = {Pattern recognition letters : an official publication of the International Association for Pattern Recognition.},
title = {Smooth Chan-Vese Segmentation via Graph Cuts},
url = {http://www.sciencedirect.com/science/article/pii/S0167865512000955},
volume = {33},
year = {2012}
}
TY - JOUR
ID - 948670
AU - Daněk, Ondřej - Matula, Pavel - Maška, Martin - Kozubek, Michal
PY - 2012
TI - Smooth Chan-Vese Segmentation via Graph Cuts
JF - Pattern recognition letters : an official publication of the International Association for Pattern Recognition.
VL - 33
IS - 10
SP - 1405-1410
EP - 1405-1410
PB - Elsevier
SN - 01678655
KW - image segmentation
KW - graph cut framework
KW - Chan-Vese model
KW - boundary smoothness
KW - memory consumption
UR - http://www.sciencedirect.com/science/article/pii/S0167865512000955
L2 - http://www.sciencedirect.com/science/article/pii/S0167865512000955
N2 - The graph cut framework presents an efficient method for approximating the minimum of the popular Chan-Vese functional for image segmentation. However, a fundamental drawback of graph cuts is a need for a dense neighbourhood system in order to avoid geometric artefacts and jagged boundaries. The increasing connectivity leads to excessive memory consumption and burdens the efficiency of the method. In this paper, we address the issue by introducing a two-stage connectivity scaling approach. First, coarse segmentation is calculated using a sparse neighbourhood over the whole image. In the second stage, the segmentation is refined by employing a dense neighbourhood in a narrow band around the boundary from the first stage. We demonstrate that this method fits well with the Chan-Vese functional and yields smooth boundaries without increasing the computational demands significantly. Moreover, under specific conditions, the construction has no negative effect on the optimality of the solution.
ER -
DANĚK, Ondřej, Pavel MATULA, Martin MAŠKA a Michal KOZUBEK. Smooth Chan-Vese Segmentation via Graph Cuts. \textit{Pattern recognition letters : an official publication of the International Association for Pattern Recognition.}. Amsterdam: Elsevier, 2012, roč.~33, č.~10, s.~1405-1410. ISSN~0167-8655. Dostupné z: https://dx.doi.org/10.1016/j.patrec.2012.03.013.
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