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A neural-learning-based reflectance model for 3-D shape reconstruction

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2 Author(s)
Siu-Yeung Cho ; Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, China ; Chow, T.W.S.

In this letter, the limitation of the conventional Lambertian reflectance model is addressed and a new neural-based reflectance model is proposed of which the physical parameters of the reflectivity under different lighting conditions are interpreted by the neural network behavior of the nonlinear input-output mapping. The idea of this method is to optimize a proper reflectance model by a neural learning algorithm and to recover the object surface by a simple shape-from-shading (SFS) variational method with this neural-based model. A unified computational scheme is proposed to yield the best SFS solution. This SFS technique has become more robust for most objects, even when the lighting conditions are uncertain.

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Industrial Electronics, IEEE Transactions on  (Volume:47 ,  Issue: 6 )