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Descripción
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| 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. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Journal of Real-Time Image Processing |
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ISSN
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1861-8200 |
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Factor de impacto JCR
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1,156 |
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Información de impacto
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Datos JCR del año 2012 |
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Volumen
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DOI
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10.1007/s11554-012-0314-1 |
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Número de revista
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Desde la página
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1 |
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Hasta la página
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14 |
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Mes
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ENERO |
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Ranking
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