Learning parametric specular reflectance model by radial basis function network

Siu Yeung Cho, Tommy W.S. Chow

Research output: Journal PublicationArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1498-1503
Number of pages6
JournalIEEE Transactions on Neural Networks
Issue number6
Publication statusPublished - 2000
Externally publishedYes


  • Radial basis function (RBF)
  • Shape from shading (SFS)
  • Specular reflectance model

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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