A Convex Optimization Approach for Depth Estimation Under Illumination Variation

Abstract : Illumination changes cause serious problems in many computer vision applications. We present a new method for addressing robust depth estimation from a stereo pair under varying illumination conditions. First, a spatially varying multiplicative model is developed to account for brightness changes induced between left and right views. The depth estimation problem, based on this model, is then formulated as a constrained optimization problem in which an appropriate convex objective function is minimized under various convex constraints modelling prior knowledge and observed information. The resulting multiconstrained optimization problem is finally solved via a parallel block iterative algorithm which offers great flexibility in the incorporation of several constraints. Experimental results on both synthetic and real stereo pairs demonstrate the good performance of our method to efficiently recover depth and illumination variation fields, simultaneously.
Type de document :
Article dans une revue
IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2009, 18 (4), pp.813--830. 〈10.1109/TIP.2008.2011386〉
Liste complète des métadonnées

https://hal-upec-upem.archives-ouvertes.fr/hal-00692900
Contributeur : Jean-Christophe Pesquet <>
Soumis le : mardi 1 mai 2012 - 16:48:52
Dernière modification le : mercredi 15 avril 2015 - 16:07:01

Identifiants

Citation

Wided Miled, Jean-Christophe Pesquet, Michel Parent. A Convex Optimization Approach for Depth Estimation Under Illumination Variation. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2009, 18 (4), pp.813--830. 〈10.1109/TIP.2008.2011386〉. 〈hal-00692900〉

Partager

Métriques

Consultations de la notice

91