Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33350
Title: A neural-learning-based reflectance model for 3-D shape reconstruction
Authors: Cho, SY
Chow, TW
Keywords: Image reconstruction
Learning (artificial intelligence)
Light reflection
Neural nets
Reflectivity
Issue Date: 2000
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on industrial electronics, 2000, v. 47, no. 6, p. 1346-1350 How to cite?
Journal: IEEE transactions on industrial electronics 
Abstract: 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.
URI: http://hdl.handle.net/10397/33350
ISSN: 0278-0046
EISSN: 1557-9948
DOI: 10.1109/41.887964
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