Illumination estimation and cast shadow detection through a higher-order graphical model
| Autor: | Chaohui Wang, Nikos Paragios, Dimitris Samaras, Alexandros Panagopoulos |
|---|---|
| Přispěvatelé: | Computer Science Department [SUNY], Stony Brook University [SUNY] (SBU), State University of New York (SUNY)-State University of New York (SUNY), Organ Modeling through Extraction, Representation and Understanding of Medical Image Content (GALEN), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Ecole Centrale Paris, State University of New York (SUNY), Mathématiques Appliquées aux Systèmes - EA 4037 (MAS), Ecole Centrale Paris, imagine [Marne-la-Vallée], Laboratoire d'Informatique Gaspard-Monge (LIGM), Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)-Centre Scientifique et Technique du Bâtiment (CSTB), Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS), Université Paris-Est Marne-la-Vallée (UPEM), École des Ponts ParisTech (ENPC), Ecole Centrale Paris-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre Scientifique et Technique du Bâtiment (CSTB)-École des Ponts ParisTech (ENPC)-Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM)-Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-Université Paris-Est Marne-la-Vallée (UPEM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM) |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Markov random field
business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Markov process [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 02 engineering and technology Object detection 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences symbols.namesake 0302 clinical medicine Shadow 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Computer vision Graphical model Artificial intelligence business Energy (signal processing) ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011) : Colorado Springs, Colorado, USA, 20-25 June 2011 2011 IEEE Conference on Computer Vision and Pattern Recognition-CVPR 2011 2011 IEEE Conference on Computer Vision and Pattern Recognition-CVPR 2011, Jun 2011, Colorado Springs, United States. pp.673-680, ⟨10.1109/CVPR.2011.5995585⟩ CVPR |
| Popis: | International audience; In this paper, we propose a novel framework to jointly recover the illumination environment and an estimate of the cast shadows in a scene from a single image, given coarse 3D geometry. We describe a higher-order Markov Random Field (MRF) illumination model, which combines low-level shadow evidence with high-level prior knowledge for the joint estimation of cast shadows and the illumination environment. First, a rough illumination estimate and the structure of the graphical model in the illumination space is determined through a voting procedure. Then, a higher order approach is considered where illumination sources are coupled with the observed image and the latent variables corresponding to the shadow detection. We examine two inference methods in order to effectively minimize the MRF energy of our model. Experimental evaluation shows that our approach is robust to rough knowledge of geometry and reflectance and inaccurate initial shadow estimates. We demonstrate the power of our MRF illumination model on various datasets and show that we can estimate the illumination in images of objects belonging to the same class using the same coarse 3D model to represent all instances of the class. |
| Databáze: | OpenAIRE |
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