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Spectral dichromatic parameter recovery from two views via total variation hyper-priors
conference contribution
posted on 2017-01-01, 00:00 authored by Filippo Bergamasco, Andrea Torsello, Antonio Robles-KellyAntonio Robles-KellyIn this paper, we propose an approach for the recovery of the dichromatic model from two hyperspectral or multispectral images, i.e., the joint estimation of illuminant, reflectance, and shading of each pixel, as well as the optical flow between the two views. The approach is based on the minimization of an energy functional linking the dichromatic model to the image appearances and the flow between the images to the factorized reflectance component. In order to minimize the resulting under-constrained problem, we apply vectorial total variation regularizers both to the scene reflectance, and to the flow hyper-parameters. We do this by enforcing the physical priors for the reflectance of the materials in the scene and assuming the flow varies smoothly within rigid objects in the image. We show the effectiveness of the approach compared with single view model recovery both in terms of model constancy and of closeness to the ground truth.
History
Event
Asian Federation of Computer Vision. Conference (13th : 2016 : Taipei, Taiwan)Volume
10116Series
Asian Federation of Computer Vision ConferencePagination
317 - 333Publisher
SpringerLocation
Taipei, TaiwanPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2016-11-20End date
2016-11-24ISBN-13
978-3-319-54407-6Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2017, Springer International Publishing AGEditor/Contributor(s)
C Chen, J Lu, K MaTitle of proceedings
ACCV 2016 : Proceedings of the 13th Asian Conference on Computer VisionUsage metrics
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No categories selectedKeywords
Spectral dichromatic parameter recoveryTotal variation hyper-priorsDichromatic modelHyperspectral imagesMultispectral imagesScience & TechnologyTechnologyComputer Science, Interdisciplinary ApplicationsComputer Science, Theory & MethodsImaging Science & Photographic TechnologyComputer ScienceCOLOR CONSTANCYREFLECTION COMPONENTSMODELCONSTRAINTS
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