IEICE TRANSACTIONS on Information
Robust Method for Recovering Sign of Gaussian Curvature from Multiple Shading Images
Summary :
This paper proposes a new method to recover the sign of local Gaussian curvature from multiple (more than three) shading images. The information required to recover the sign of Gaussian curvature is obtained by applying Principal Components Analysis (PCA) to the normalized irradiance measurements. The sign of the Gaussian curvature is recovered based on the relative orientation of measurements obtained on a local five point test pattern to those in the 2-D subspace called the eigen plane. Using multiple shading images gives a more accurate and robust result and minimizes the effect of shadows by allowing a larger area of the visible surface to be analyzed compared to methods using only three shading images. Furthermore, it allows the method to be applied to specular surfaces. Since PCA removes linear correlation among images, the method can produce results of high quality even when the light source directions are not widely dispersed.
- Publication
- IEICE TRANSACTIONS on Information Vol.E84-D No.12 pp.1633-1641
- Publication Date
- 2001/12/01
- Publicized
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- Type of Manuscript
- Special Section PAPER (Special Issue on Machine Vision Applications)
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Shinji FUKUI, Yuji IWAHORI, Robert J. WOODHAM, Kenji FUNAHASHI, Akira IWATA, "Robust Method for Recovering Sign of Gaussian Curvature from Multiple Shading Images" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 12, pp. 1633-1641, December 2001, doi: .
Abstract: This paper proposes a new method to recover the sign of local Gaussian curvature from multiple (more than three) shading images. The information required to recover the sign of Gaussian curvature is obtained by applying Principal Components Analysis (PCA) to the normalized irradiance measurements. The sign of the Gaussian curvature is recovered based on the relative orientation of measurements obtained on a local five point test pattern to those in the 2-D subspace called the eigen plane. Using multiple shading images gives a more accurate and robust result and minimizes the effect of shadows by allowing a larger area of the visible surface to be analyzed compared to methods using only three shading images. Furthermore, it allows the method to be applied to specular surfaces. Since PCA removes linear correlation among images, the method can produce results of high quality even when the light source directions are not widely dispersed.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_12_1633/_p
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@ARTICLE{e84-d_12_1633,
author={Shinji FUKUI, Yuji IWAHORI, Robert J. WOODHAM, Kenji FUNAHASHI, Akira IWATA, },
journal={IEICE TRANSACTIONS on Information},
title={Robust Method for Recovering Sign of Gaussian Curvature from Multiple Shading Images},
year={2001},
volume={E84-D},
number={12},
pages={1633-1641},
abstract={This paper proposes a new method to recover the sign of local Gaussian curvature from multiple (more than three) shading images. The information required to recover the sign of Gaussian curvature is obtained by applying Principal Components Analysis (PCA) to the normalized irradiance measurements. The sign of the Gaussian curvature is recovered based on the relative orientation of measurements obtained on a local five point test pattern to those in the 2-D subspace called the eigen plane. Using multiple shading images gives a more accurate and robust result and minimizes the effect of shadows by allowing a larger area of the visible surface to be analyzed compared to methods using only three shading images. Furthermore, it allows the method to be applied to specular surfaces. Since PCA removes linear correlation among images, the method can produce results of high quality even when the light source directions are not widely dispersed.},
keywords={},
doi={},
ISSN={},
month={December},}
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TY - JOUR
TI - Robust Method for Recovering Sign of Gaussian Curvature from Multiple Shading Images
T2 - IEICE TRANSACTIONS on Information
SP - 1633
EP - 1641
AU - Shinji FUKUI
AU - Yuji IWAHORI
AU - Robert J. WOODHAM
AU - Kenji FUNAHASHI
AU - Akira IWATA
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2001
AB - This paper proposes a new method to recover the sign of local Gaussian curvature from multiple (more than three) shading images. The information required to recover the sign of Gaussian curvature is obtained by applying Principal Components Analysis (PCA) to the normalized irradiance measurements. The sign of the Gaussian curvature is recovered based on the relative orientation of measurements obtained on a local five point test pattern to those in the 2-D subspace called the eigen plane. Using multiple shading images gives a more accurate and robust result and minimizes the effect of shadows by allowing a larger area of the visible surface to be analyzed compared to methods using only three shading images. Furthermore, it allows the method to be applied to specular surfaces. Since PCA removes linear correlation among images, the method can produce results of high quality even when the light source directions are not widely dispersed.
ER -
English
