Learning-based tone mapping operator for efficient image matching - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Multimedia Année : 2019

Learning-based tone mapping operator for efficient image matching

Résumé

In this paper, we propose a new framework to optimally tone map the high dynamic range (HDR) content for image matching under drastic illumination variations. Since tone mapping operators (TMO) have traditionally been used for displaying HDR scenes, their design is suboptimal when used for computer vision tasks such as image matching. We address this sub-optimality by proposing a two-step framework, consisting of: a) a luminance-invariant guidance model based on a Support Vector Regressor (SVR) to optimally adapt the tone mapping function for image matching; and b) an energy maximization model to generate appropriate training samples for learning the SVR. At each step, we collectively address both stages of keypoint detection and descriptor extraction in the feature matching framework. By locally altering the intrinsic characteristics of the tone mapping function, the learned guid- ance model facilitates the extraction of local invariant features in the presence of illumination variations. We demonstrate that the proposed TMO significantly outperforms perceptually-driven state-of-the-art TMOs on a dataset of HDR scenes characterized by challenging lighting variations, such as day/night transitions.
Fichier principal
Vignette du fichier
2019_TMM_Rana_et_al.pdf (12.06 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01716965 , version 1 (10-01-2020)

Identifiants

Citer

Aakanksha A Rana, Giuseppe Valenzise, Frédéric Dufaux. Learning-based tone mapping operator for efficient image matching. IEEE Transactions on Multimedia, 2019, 21 (1), pp.256-268. ⟨10.1109/TMM.2018.2839885⟩. ⟨hal-01716965⟩
255 Consultations
118 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More