An Application to Photometric Stereo by Neural Networks

Article type: Research Article

Authors: Iwahori, Yuji | Ishii, Naobiro | Woodham, Robert J. | Ozaki, Masahiro | Adachi, Yoshinori

Affiliations: Faculty of Engineering, Nagoya lnstitute of Technology, Japan | Dept. of Computer Science, University of British Columbia, Canada | Okazaki Women's Jr. College, Japan | Chubu University, Japan

Abstract: This article proposes a new idea to determine surface gradients uniquely by using neural networks that can learn any reflectance maps. The Phong illuminating function is used to represent the glossy surface, including Lambertian surfaces, and it includes three parameters that characterize the reflectance property of the object susrface. This article shows that Phong reflectance functions from three different directions can be learned through neural networks by treating the values of three image irradiances as inputs while treating the corresponding two surface gradient parameters as outputs. Computer simulation was demonstrated for three layered networks. Learning was done for a spherical object and it was repeated by the back-propagation algorithm. The desirable surface gradients could be recovered by neural networks when any triples of image irradiances were inputed as a test pattern. Neural networks have a great capacity to store the three reflectance maps, and this method offers the advantage that special analysis to solve the simultaneous equations is not required as in the conventional method.

DOI: 10.3233/IFS-1994-2105

Journal: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 1, pp. 69-73, 1994