IEICE TRANSACTIONS on Information
Classification of Surface Curvature from Shading Images Using Neural Network
Summary :
This paper proposes a new approach to recover the sign of local surface curvature of object from three shading images using neural network. The RBF (Radial Basis Function) neural network is used to learn the mapping of three image irradiances to the position on a sphere. Then, the learned neural network maps the image irradiances at the neighbor pixels of the test object taken from three illuminating directions of light sources onto the sphere images taken under the same illuminating condition. Using the property that basic six kinds of surface curvature has the different relative locations of the local five points mapped on the sphere, not only the Gaussian curvature but also the kind of curvature is directly recovered locally from the relation of the locations on the mapped points on the sphere without knowing the values of surface gradient for each point. Further, two step neural networks which combines the forward mapping and its inverse mapping one can be used to get the local confidence estimate for the obtained results. The entire approach is non-parametric, empirical in that no explicit assumptions are made about light source directions or surface reflectance. Results are demonstrated by the experiments for real images.
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- IEICE TRANSACTIONS on Information Vol.E81-D No.8 pp.889-900
- Publication Date
- 1998/08/25
- Publicized
- Online ISSN
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- Type of Manuscript
- Category
- Image Processing,Computer Graphics and Pattern Recognition
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Yuji IWAHORI, Shinji FUKUI, Robert J. WOODHAM, Akira IWATA, "Classification of Surface Curvature from Shading Images Using Neural Network" in IEICE TRANSACTIONS on Information,
vol. E81-D, no. 8, pp. 889-900, August 1998, doi: .
Abstract: This paper proposes a new approach to recover the sign of local surface curvature of object from three shading images using neural network. The RBF (Radial Basis Function) neural network is used to learn the mapping of three image irradiances to the position on a sphere. Then, the learned neural network maps the image irradiances at the neighbor pixels of the test object taken from three illuminating directions of light sources onto the sphere images taken under the same illuminating condition. Using the property that basic six kinds of surface curvature has the different relative locations of the local five points mapped on the sphere, not only the Gaussian curvature but also the kind of curvature is directly recovered locally from the relation of the locations on the mapped points on the sphere without knowing the values of surface gradient for each point. Further, two step neural networks which combines the forward mapping and its inverse mapping one can be used to get the local confidence estimate for the obtained results. The entire approach is non-parametric, empirical in that no explicit assumptions are made about light source directions or surface reflectance. Results are demonstrated by the experiments for real images.
URL: https://global.ieice.org/en_transactions/information/10.1587/e81-d_8_889/_p
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@ARTICLE{e81-d_8_889,
author={Yuji IWAHORI, Shinji FUKUI, Robert J. WOODHAM, Akira IWATA, },
journal={IEICE TRANSACTIONS on Information},
title={Classification of Surface Curvature from Shading Images Using Neural Network},
year={1998},
volume={E81-D},
number={8},
pages={889-900},
abstract={This paper proposes a new approach to recover the sign of local surface curvature of object from three shading images using neural network. The RBF (Radial Basis Function) neural network is used to learn the mapping of three image irradiances to the position on a sphere. Then, the learned neural network maps the image irradiances at the neighbor pixels of the test object taken from three illuminating directions of light sources onto the sphere images taken under the same illuminating condition. Using the property that basic six kinds of surface curvature has the different relative locations of the local five points mapped on the sphere, not only the Gaussian curvature but also the kind of curvature is directly recovered locally from the relation of the locations on the mapped points on the sphere without knowing the values of surface gradient for each point. Further, two step neural networks which combines the forward mapping and its inverse mapping one can be used to get the local confidence estimate for the obtained results. The entire approach is non-parametric, empirical in that no explicit assumptions are made about light source directions or surface reflectance. Results are demonstrated by the experiments for real images.},
keywords={},
doi={},
ISSN={},
month={August},}
Copy
TY - JOUR
TI - Classification of Surface Curvature from Shading Images Using Neural Network
T2 - IEICE TRANSACTIONS on Information
SP - 889
EP - 900
AU - Yuji IWAHORI
AU - Shinji FUKUI
AU - Robert J. WOODHAM
AU - Akira IWATA
PY - 1998
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E81-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 1998
AB - This paper proposes a new approach to recover the sign of local surface curvature of object from three shading images using neural network. The RBF (Radial Basis Function) neural network is used to learn the mapping of three image irradiances to the position on a sphere. Then, the learned neural network maps the image irradiances at the neighbor pixels of the test object taken from three illuminating directions of light sources onto the sphere images taken under the same illuminating condition. Using the property that basic six kinds of surface curvature has the different relative locations of the local five points mapped on the sphere, not only the Gaussian curvature but also the kind of curvature is directly recovered locally from the relation of the locations on the mapped points on the sphere without knowing the values of surface gradient for each point. Further, two step neural networks which combines the forward mapping and its inverse mapping one can be used to get the local confidence estimate for the obtained results. The entire approach is non-parametric, empirical in that no explicit assumptions are made about light source directions or surface reflectance. Results are demonstrated by the experiments for real images.
ER -
English
