An Evaluation of HDR Image Matching under Extreme Illumination Changes
Résumé
High dynamic range (HDR) imaging has potential to facili-
tate computer vision tasks such as image matching where lighting trans-
formations hinder the matching performance. However, little has been
done to quantify the gains with different possible HDR representations
for vision algorithms like feature extraction. In this paper, we evaluate the
performance of the full feature extraction pipeline, including detection
and description, on ten different image representations: low dynamic
range (LDR), seven different tone mapped (TM) HDR and two HDR
imaging (linear and log encoded) representations. We measure the impact
of using these different representations for feature matching using mean
average precision (mAP) scores on four illumination change datasets. We
perform feature extraction using four popular schemes in the literature:
SIFT, SURF, BRISK, FREAK. With respect to previous studies, our
observations confirm the advantages of HDR over conventional LDR
imagery, and the fact that HDR linear values are not appropriate for
vision tasks. However, HDR representations that work best for keypoint
detection are not necessarily optimal when the full feature extraction is
taken into account.
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