Anisotropic mesh adaptation for region-based segmentation accounting for image spatial information
Visualitza/Obre
10.1016/j.camwa.2022.06.025
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/377277
Tipus de documentArticle
Data publicació2022-09-01
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
Abstract
A finite element-based image segmentation strategy enhanced by an anisotropic mesh adaptation procedure is presented. The methodology relies on a split Bregman algorithm for the minimisation of a region-based energy functional and on an anisotropic recovery-based error estimate to drive mesh adaptation. More precisely, a Bayesian energy functional is considered to account for image spatial information, ensuring that the methodology is able to identify inhomogeneous spatial patterns in complex images. In addition, the anisotropic mesh adaptation guarantees a sharp detection of the interface between background and foreground of the image, with a reduced number of degrees of freedom. The resulting split-adapt Bregman algorithm is tested on a set of real images showing the accuracy and robustness of the method, even in the presence of Gaussian, salt and pepper and speckle noise.
Descripció
DOI link format: http://dx.doi.org/10.1016/j.camwa.2022.06.025
© 2022 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
CitacióGiacomini, M.; Perotto, S. Anisotropic mesh adaptation for region-based segmentation accounting for image spatial information. "Computers and Mathematics with Applications", 1 Setembre 2022, vol. 121, p. 1-17.
ISSN0097-4943
Versió de l'editorhttps://www.sciencedirect.com/science/article/pii/S0898122122002711
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