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Geodesics via asymmetric Hear diffusion based on Finsler Metric

Cohen, Laurent D.; Yang, Fang; Chai, Li; Chen, Da (2018), Geodesics via asymmetric Hear diffusion based on Finsler Metric, Computer Vision – ACCV 2018, Springer : Berlin Heidelberg, p. 371-386

Type
Communication / Conférence
External document link
https://hal.archives-ouvertes.fr/hal-02281619
Date
2018
Conference title
14th Asian Conference on Computer Vision
Conference date
2018-12
Conference city
Perth
Conference country
Australia
Book title
Computer Vision – ACCV 2018
Publisher
Springer
Published in
Berlin Heidelberg
ISBN
978-3-030-20872-1
Number of pages
XX, 727
Pages
371-386
Metadata
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Author(s)
Cohen, Laurent D.
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Yang, Fang
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Chai, Li

Chen, Da
Abstract (EN)
Current image segmentation involves strongly non-uniform, anisotropic and asymmetric measures of path length, which challenges available algorithms. In order to meet these challenges, this paper applies the Finsler metric to the geodesic method based on heat diffusion. This metric is non-Riemannian, anisotropic and asymmetric, which helps the heat to flow more on the features of interest. Experiments demonstrate the feasibility of the proposed method. The experimental results show that our algorithm is of strong robustness and effectiveness. The proposed method can be applied to contour detection and tubular structure segmentation in images, such as vessel segmentation in medical images and road extraction in satellite images and so on.
Subjects / Keywords
geodesic; Finsler Metric

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