| Texto completo | |
| Autor(es): |
Scabini, Leonardo F. S.
;
Fistarol, Danilo O.
;
Cantero, Savio V.
;
Goncalves, Wesley N.
;
Machado, Bruno Brandoli
;
Rodrigues, Jr., Jose F.
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | EXPERT SYSTEMS WITH APPLICATIONS; v. 89, p. 362-373, DEC 15 2017. |
| Citações Web of Science: | 4 |
| Resumo | |
We introduce a method for shape recognition based on the angular analysis of Complex Networks. Our method models shapes as Complex Networks defining a more descriptive representation of the inner angularity of the shape's perimeter. The result is a set of measures that better describe shapes if compared to previous approaches that use only the vertices' degree. We extract the angle between the Complex Network edges, and then we analyze their distribution along with a network dynamic evolution. The proposed approach, named Angular Descriptors of Complex Networks (ADCN), presents a high discriminatory power, as evidenced by experiments conducted in five datasets. It is rotation invariant, presents high robustness against scale changes and degradation levels, overcoming traditional methods such as Zernike moments, Multiscale Fractal dimension, Fourier, Curvature and the degree-based descriptors of Complex Networks. (C) 2017 Elsevier Ltd. All rights reserved. (AU) | |
| Processo FAPESP: | 16/02557-0 - Processamento analítico de grandes grafos: identificação de padrões para o suporte à decisão na Web 2.0 |
| Beneficiário: | José Fernando Rodrigues Júnior |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |