Learning-based Adaptive Tone Mapping for Keypoint Detection
Résumé
The goal of tone mapping operators (TMOs) has traditionally been
to display high dynamic range (HDR) pictures in a perceptually favorable
way. However, when tone-mapped images are to be used
for computer vision tasks such as keypoint detection, these design
approaches are suboptimal. In this paper, we propose a new
learning-based adaptive tone mapping framework which aims at enhancing
keypoint stability under drastic illumination variations. To
this end, we design a pixel-wise adaptive TMO which is modulated
based on a model derived by Support Vector Regression (SVR) using
local higher order characteristics. To circumvent the difficulty
to train SVR in this context, we further propose a simple detection similarity-maximization
model to generate appropriate training samples
using multiple images undergoing illumination transformations.
We evaluate the performance of our proposed framework in terms
of keypoint repeatability for state-of-the-art keypoint detectors. Experimental
results show that our proposed learning-based adaptive
TMO yields higher keypoint stability when compared to existing
perceptually-driven state-of-the-art TMOs.
Origine | Fichiers produits par l'(les) auteur(s) |
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