Neural Network Based Photometric Stereo with a Nearby Rotational Moving Light Source
Summary :
An implementation of photometric stereo is described in which all directions of illumination are close to and rotationally symmetric about the viewing direction. THis has practical value but gives rise to a problem that is numerically ill-conditioned. Ill-conditioning is overcome in two ways. First, many more than the theoretical minimum number of images are acquired. Second, principal components analysis (PCA) is used as a linear preprocessing technique to determine a reduced dimensionality subspace to use as input. The approach is empirical. The ability of a radial basis function (RBF) neural network to do non-parametric functional approximation is exploited. One network maps image irradiance to surface normal. A second network maps surface normal to image irradiance. The two networks are trained using samples from a calibration sphere. Comparison between the actual input and the inversely predicted input is used as a confidence estimate. Results on real data are demonstrated.
- Publication
- IEICE TRANSACTIONS on Information Vol.E80-D No.9 pp.948-957
- Publication Date
- 1997/09/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Category
- Image Processing,Computer Graphics and Pattern Recognition
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Yuji IWAHORI, Robert J. WOODHAM, Masahiro OZAKI, Hidekazu TANAKA, Naohiro ISHII, "Neural Network Based Photometric Stereo with a Nearby Rotational Moving Light Source" in IEICE TRANSACTIONS on Information,
vol. E80-D, no. 9, pp. 948-957, September 1997, doi: .
Abstract: An implementation of photometric stereo is described in which all directions of illumination are close to and rotationally symmetric about the viewing direction. THis has practical value but gives rise to a problem that is numerically ill-conditioned. Ill-conditioning is overcome in two ways. First, many more than the theoretical minimum number of images are acquired. Second, principal components analysis (PCA) is used as a linear preprocessing technique to determine a reduced dimensionality subspace to use as input. The approach is empirical. The ability of a radial basis function (RBF) neural network to do non-parametric functional approximation is exploited. One network maps image irradiance to surface normal. A second network maps surface normal to image irradiance. The two networks are trained using samples from a calibration sphere. Comparison between the actual input and the inversely predicted input is used as a confidence estimate. Results on real data are demonstrated.
URL: https://global.ieice.org/en_transactions/information/10.1587/e80-d_9_948/_p
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@ARTICLE{e80-d_9_948,
author={Yuji IWAHORI, Robert J. WOODHAM, Masahiro OZAKI, Hidekazu TANAKA, Naohiro ISHII, },
journal={IEICE TRANSACTIONS on Information},
title={Neural Network Based Photometric Stereo with a Nearby Rotational Moving Light Source},
year={1997},
volume={E80-D},
number={9},
pages={948-957},
abstract={An implementation of photometric stereo is described in which all directions of illumination are close to and rotationally symmetric about the viewing direction. THis has practical value but gives rise to a problem that is numerically ill-conditioned. Ill-conditioning is overcome in two ways. First, many more than the theoretical minimum number of images are acquired. Second, principal components analysis (PCA) is used as a linear preprocessing technique to determine a reduced dimensionality subspace to use as input. The approach is empirical. The ability of a radial basis function (RBF) neural network to do non-parametric functional approximation is exploited. One network maps image irradiance to surface normal. A second network maps surface normal to image irradiance. The two networks are trained using samples from a calibration sphere. Comparison between the actual input and the inversely predicted input is used as a confidence estimate. Results on real data are demonstrated.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Neural Network Based Photometric Stereo with a Nearby Rotational Moving Light Source
T2 - IEICE TRANSACTIONS on Information
SP - 948
EP - 957
AU - Yuji IWAHORI
AU - Robert J. WOODHAM
AU - Masahiro OZAKI
AU - Hidekazu TANAKA
AU - Naohiro ISHII
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E80-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 1997
AB - An implementation of photometric stereo is described in which all directions of illumination are close to and rotationally symmetric about the viewing direction. THis has practical value but gives rise to a problem that is numerically ill-conditioned. Ill-conditioning is overcome in two ways. First, many more than the theoretical minimum number of images are acquired. Second, principal components analysis (PCA) is used as a linear preprocessing technique to determine a reduced dimensionality subspace to use as input. The approach is empirical. The ability of a radial basis function (RBF) neural network to do non-parametric functional approximation is exploited. One network maps image irradiance to surface normal. A second network maps surface normal to image irradiance. The two networks are trained using samples from a calibration sphere. Comparison between the actual input and the inversely predicted input is used as a confidence estimate. Results on real data are demonstrated.
ER -
English
