Image segmentation by means of complex networks centrality indices

Fenu C.
2021-01-01

Abstract

In this paper, we propose a novel approach to perform multi-label segmentation. Starting from an image, we construct the associated weighted graph and assign to a small number of pixels (seeds) a label. The aim is to assign each unlabeled pixel to a label in order to identify different regions. Our algorithm uses the notion of communicability from complex networks theory to compute the easiness for an unlabeled pixel to reach a labeled one. By assigning each pixel to the label for which the greatest communicability is calculated, we can perform good image segmentation.
2021
Inglese
Proceedings - 2021 21st International Conference on Computational Science and Its Applications, ICCSA 2021
978-1-6654-5843-6
Institute of Electrical and Electronics Engineers Inc.
Alessandro Buccini, Caterina Fenu
54
59
6
21st International Conference on Computational Science and Its Applications, ICCSA 2021
Esperti anonimi
2021
ita
internazionale
scientifica
centrality indices
complex networks
image segmentation
medical imaging
no
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Fenu, C.
273
1
4.1 Contributo in Atti di convegno
none
info:eu-repo/semantics/conferencePaper
Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Questionnaire and social

Share on:
Impostazioni cookie