Observatorio de I+D+i UPM

Descripción
A critical problem faced by computer vision on mobile devices is reducing the computational cost of algorithms and avoiding visual stalls. In this paper, we introduce a procedure for reducing the number of samples required for fitting a homography to a set of noisy correspondences using a random sampling method. This is achieved by means of a geometric constraint that detects invalid minimal sets. In the experiments conducted, we show that this constraint not only reduces the number of random samples at a negligible computational cost but also balances the processor workload over time preventing visual stalls. In extreme situations of very large outlier proportion and noise level, it reduces in about one order of magnitude the number of required random samples.
Internacional	Si
JCR del ISI	Si
Título de la revista	Journal of Real-Time Image Processing
ISSN	1861-8200
Factor de impacto JCR	1,156
Información de impacto	Datos JCR del año 2012
Volumen
DOI	10.1007/s11554-012-0314-1
Número de revista
Desde la página	1
Hasta la página	14
Mes	ENERO
Ranking

Esta actividad pertenece a memorias de investigación

• Autor: Pablo Márquez Neila (UPM)
• Autor: Javier López-Alberca
• Autor: José Miguel Buenaposada Biencit (UPM)
• Autor: Luis Baumela Molina (UPM)

• Creador: Departamento: Inteligencia Artificial