Learning Parametric Specular Reflectance Model By Radial Basis Function Network  

Oleh:

Cho, Siu-Yeung ; Chow, T. W. S.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 6 (2000), page 1498-1502.
Topik: functions; learning parametric; specular; reflectance; radial basis function; networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

For the shape from shading problem, it is known that most real images usually contain specular components and are affected by unknown reflectivity. In the paper, these limitations are addressed and a 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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