Novel Shape-From-Shading Methodology with Specular Reflectance Using Wavelet Networks

Authors:
Lei Yang;Jiu-Qiang Han
Affiliations:
School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China;School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

This paper proposes a novel direct 3-D reconstruction methodology namely Shape-From-Shading with specular reflectance using wavelet networks. The thought of this approach is to optimize a proper reflectance model by learning the parameters of wavelet networks. Hybrid reflection models which contain diffuse reflectance and specular reflectance are used to formulate reflectance map equation because they are prone to reality. The approach uses wavelet networks as a parametric representation of the unknown surface to be reconstructed. After the orientation expressed by the parametric form of the surface is substituted into hybrid reflection model, the shape from shading problem is formulated as minimization of the total intensity error function over the network weights. Gradient-descent method is used to update the parameters of wavelet networks. The heights of the surface can then be obtained from the wavelet networks after supervised learning. Experiments on both synthetic and real images demonstrate the performance of the proposed SFS method.