| Texto completo | |
| Autor(es): |
Pereira, Danillo R.
;
Delpiano, Jose
;
Papa, Joao P.
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING; v. N/A, p. 10-pg., 2015-05-09. |
| Resumo | |
Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work, we have proposed an optimization-based framework for such task based on social-spider optimization, harmony search, particle swarm optimization, and Nelder-Mead algorithm. The proposed framework employed the well-known large displacement optical flow (LDOF) approach as a basis algorithm over the Middlebury and Sintel public datasets, with promising results considering the baseline proposed by the authors of LDOF. (AU) | |
| Processo FAPESP: | 14/16250-9 - Sobre a otimização de parâmetros em técnicas de aprendizado de máquina: avanços e paradigmas |
| Beneficiário: | João Paulo Papa |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 13/20387-7 - Otimização de hiperparâmetros em arquiteturas de aprendizado em profundidade |
| Beneficiário: | João Paulo Papa |
| Modalidade de apoio: | Bolsas no Exterior - Pesquisa |