TY - JOUR
T1 - Sub-pixel bayesian estimation of albedo and height
AU - Shekarforoush, Hassan
AU - Berthod, Marc
AU - Zerubia, Josiane
AU - Werman, Michael
PY - 1996
Y1 - 1996
N2 - Given a set of low resolution camera images of a Lambertian surface, it is possible to reconstruct high resolution luminance and height information, when the relative displacements of the image frames are known. We have proposed iterative algorithms for recovering high resolution albedo with the knowledge of high resolution height and vice versa. The problem of surface reconstruction has been tackled in a Bayesian framework and has been formulated as one of minimizing an error function. Markov Random Fields (MRF) have been employed to characterize the a priori constraints on the solution space. As for the surface height, we have attempted a direct computation without refering to surface orientations, while increasing the resolution by camera jittering.
AB - Given a set of low resolution camera images of a Lambertian surface, it is possible to reconstruct high resolution luminance and height information, when the relative displacements of the image frames are known. We have proposed iterative algorithms for recovering high resolution albedo with the knowledge of high resolution height and vice versa. The problem of surface reconstruction has been tackled in a Bayesian framework and has been formulated as one of minimizing an error function. Markov Random Fields (MRF) have been employed to characterize the a priori constraints on the solution space. As for the surface height, we have attempted a direct computation without refering to surface orientations, while increasing the resolution by camera jittering.
UR - http://www.scopus.com/inward/record.url?scp=0030216307&partnerID=8YFLogxK
U2 - 10.1007/BF00055148
DO - 10.1007/BF00055148
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AN - SCOPUS:0030216307
SN - 0920-5691
VL - 19
SP - 289
EP - 300
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
IS - 3
ER -