Robust Method for Recovering Sign of Gaussian Curvature from Multiple Shading Images

Shinji FUKUI  Yuji IWAHORI  Robert J. WOODHAM  Kenji FUNAHASHI  Akira IWATA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E84-D   No.12   pp.1633-1641
Publication Date: 2001/12/01
Online ISSN: 
DOI: 
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Machine Vision Applications)
Category: 
Keyword: 
Gaussian curvature,  shape representation and recovery,  physics-based vision,  principal component analysis,  

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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.