TY - GEN
T1 - Spectral dichromatic parameter recovery from two views via total variation hyper-priors
AU - Bergamasco, Filippo
AU - Torsello, Andrea
AU - Robles-Kelly, Antonio
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - In 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.
AB - In 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.
UR - http://www.scopus.com/inward/record.url?scp=85016208998&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-54407-6_21
DO - 10.1007/978-3-319-54407-6_21
M3 - Conference contribution
SN - 9783319544069
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 317
EP - 333
BT - Computer Vision - ACCV 2016 Workshops - ACCV 2016 International Workshops, Revised Selected Papers
A2 - Lu, Jiwen
A2 - Ma, Kai-Kuang
A2 - Chen, Chu-Song
PB - Springer Verlag
T2 - 13th Asian Conference on Computer Vision, ACCV 2016
Y2 - 20 November 2016 through 24 November 2016
ER -