| dc.contributor.author | RODRIGUES, Paulo | |
| dc.contributor.author | LOPES, Guilherme | |
| dc.contributor.author | ERDMANN, H. R. | |
| dc.contributor.author | RIBEIRO, M. P. | |
| dc.contributor.author | GIRALDI, G. A. | |
| dc.date.accessioned | 2019-08-17T20:00:30Z | |
| dc.date.accessioned | 2022-09-21T19:50:17Z | |
| dc.date.available | 2019-08-17T20:00:30Z | |
| dc.date.available | 2022-09-21T19:50:17Z | |
| dc.date.issued | 2015 | |
| dc.identifier.citation | RODRIGUES, Paulo; LOPES, Guilherme; ERDMANN, H. R.; RIBEIRO, M. P.; GIRALDI, G. A. Improving a firefly meta-heuristic for multilevel image segmentation using Tsallis entropy. Pattern Analysis and Applications (Print), v. 1, p. 1-20, 2015. | |
| dc.identifier.issn | 1433-7541 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12032/40540 | |
| dc.description.abstract | In this paper we show that the non-extensive Tsallis entropy, when used as kernel in the bio-inspired firefly algorithm for multi-thresholding in image segmentation, is more efficient than using the traditional crossentropy resented in the literature. The firefly algorithm is a swarm-based meta-heuristic, inspired by fireflies-seeking behavior following their luminescence. We show that the use of more convex kernels, as those based on non-extensive entropy, is more effective at 5 % of significance level than the cross-entropy counterpart when applied in synthetic spaces for searching thresholds in global minimum | en |
| dc.relation.ispartof | Pattern Analysis and Applications (Print) | |
| dc.rights | Acesso Aberto | |
| dc.title | Improving a firefly meta-heuristic for multilevel image segmentation using Tsallis entropy | pt_BR |
| dc.type | Artigo | pt_BR |
