Learning-based tone mapping operator for efficient image matching

Abstract : 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.
Type de document :
Article dans une revue
IEEE Transactions on Multimedia, Institute of Electrical and Electronics Engineers, 2018, (in press), 〈10.1109/TMM.2018.2839885〉
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01716965
Contributeur : Frédéric Dufaux <>
Soumis le : dimanche 25 février 2018 - 16:48:12
Dernière modification le : mardi 27 novembre 2018 - 13:58:45

Identifiants

Citation

Aakanksha Rana, Giuseppe Valenzise, Frédéric Dufaux. Learning-based tone mapping operator for efficient image matching. IEEE Transactions on Multimedia, Institute of Electrical and Electronics Engineers, 2018, (in press), 〈10.1109/TMM.2018.2839885〉. 〈hal-01716965〉

Partager

Métriques

Consultations de la notice

187