Global Minimum for Curvature Penalized Minimal Path Method
Chen, Da; Cohen, Laurent D.; Mirebeau, Jean-Marie (2015), Global Minimum for Curvature Penalized Minimal Path Method, Proceedings of the British Machine Vision Conference (BMVC), BMVC. 10.5244/C.29.86
Type
Communication / ConférenceDate
2015Conference title
BMVC 2015 ( British Machine Vision Conference)Conference date
2015-09Conference city
SwanseaConference country
United KingdomBook title
Proceedings of the British Machine Vision Conference (BMVC)Publisher
BMVC
Publication identifier
Metadata
Show full item recordAuthor(s)
Chen, DaCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Cohen, Laurent D.
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Mirebeau, Jean-Marie
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
Minimal path or geodesic methods have been widely applied to image analysis and medical imaging. However, traditional minimal path methods do not consider the effect of the curvature. In this paper, we propose a novel curvature penalized minimal path approach implemented via the anisotropic fast marching method and asymmetric Finsler metrics. We study the weighted Euler's elastica based geodesic energy and give an approximation to this energy by an orientation-lifted Finsler metric so that the proposed model can achieve a global minimum of this geodesic energy between the endpoint and initial source point. We also introduce a method to simplify the initialization of the proposed model. Experiments show that the proposed curvature penalized minimal path model owns several advantages comparing to the existed state-of-the-art minimal path models without curvature penalty both on synthetic and real images.Subjects / Keywords
Medical ApplicationsRelated items
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