On the optical flow model selection through metaheuristics

Danillo R. Pereira, José Delpiano, João P. Papa

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

5 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Número de artículo11
PublicaciónEurasip Journal on Image and Video Processing
Volumen2015
N.º1
DOI
EstadoPublicada - 26 dic 2015

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Publisher Copyright:
© 2015, Pereira et al.; licensee Springer.

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