Anisotropic mesh adaptation for region-based segmentation accounting for image spatial information
Year: 2022
Type: article
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.
Authors: Matteo Giacomini, Simona Perotto
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,070
Funders: Istituto Nazionale di Alta Matematica "Francesco Severi", Associazione di Fondazioni e di Casse di Risparmio, Generalitat de Catalunya, Ministerio de Ciencia e Innovación, Politecnico di Milano, Yukawa Institute for Theoretical Physics, Kyoto University, Gruppo Nazionale per il Calcolo Scientifico, Agencia Estatal de Investigación
Awards: G1155339745, G1175115578, G121809054, G1855990336, G2064873908, G2262748287, G2309174360, G2462415295, G2660406951, G2741172043, G279363373, G3005324705, G300979063, G3209530734, G3433098972, G34346518, G3480869486, G3484868581, G3518538256, G3546882995, G425668148, G430777504, G4526231891, G5080475149, G5421974903, G5805502524, G6841463731, G69051668, G7084143925, G897484643, G99296377
OpenAlex ID: W4226523760