Optimizing Tone Mapping Operators for Keypoint Detection under Illumination Changes
Résumé
Tone mapping operators (TMO) have recently raised interest
for their capability to handle illumination changes.
However, these TMOs are optimized with respect to perception
rather than image analysis tasks like keypoint detection.
Moreover, no work has been done to analyze the factors affecting
the optimization of TMOs for such tasks. In this paper,
we investigate the influence of two factors– Correlation Coefficient
(CC) and Repeatability Rate (RR) of the tone mapped
images for the optimization of classical Retinex based models
to enhance keypoint detection under illumination changes.
CC-based optimized models aim at increasing the similarity
of the tone mapped images. Conversely, RR-based optimized
models quantify the optimal detection performance gains. By
considering two simple Retinex based models, i.e., Gaussian
and bilateral filtering, we show that estimating as precisely
as possible the illumination, CC-based optimized models do
not necessarily bring to optimal keypoint detection performance.
We conclude that, instead, other criteria specific to
RR-based optimized models should be taken into account.
Moreover, large gains in performance with respect to existing
popular TMOs motivate further research towards optimal
tone mapping technique for computer vision applications.
Origine : Fichiers produits par l'(les) auteur(s)
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