Learning parametric specular reflectance model by radial basis function network.
IEEE Trans Neural Netw
; 11(6): 1498-503, 2000.
Article
en En
| MEDLINE
| ID: mdl-18249875
ABSTRACT
For the shape from shading problem, it is known that most real images usually contain specular components and are affected by unknown reflectivity. In this paper, these limitations are addressed and a new neural-based specular reflectance model is proposed. The idea of this method is to optimize a proper specular model by learning the parameters of a radial basis function network and to recover the object shape by the variational approach with this resulting model. The obtained results are very encouraging and the performance is demonstrated by using the synthetic and real images in the case of different specular effects and noisy environments.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
IEEE Trans Neural Netw
Asunto de la revista:
INFORMATICA MEDICA
Año:
2000
Tipo del documento:
Article
Pais de publicación:
EEUU
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ESTADOS UNIDOS
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ESTADOS UNIDOS DA AMERICA
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EUA
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UNITED STATES
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UNITED STATES OF AMERICA
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US
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USA