Please use this identifier to cite or link to this item:
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
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.
ISSN: 0278-0046
EISSN: 1557-9948
DOI: 10.1109/41.887964
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record


Citations as of Feb 12, 2016

Page view(s)

Last Week
Last month
Checked on Aug 13, 2017

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.