Presentation 1993/9/22
Extension of the Standard Regularization Theory into Multi-valued Functions:Recovery of Multiple Surfaces
Masahiko Shizawa,
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Abstract(in English) An extension of the standard regularization theory is proposed for data approximations by multi-valued functions which are essential for such as the transparency problems in computational vision. In this paper,by using a direct representation for multi-fold surfaces based on tensor product,we show that the data approximation by a multi-valued function can be reduced to minimization of a single quadric functional.Therefore,since the Euler-Lagrange equation of the functional becomes linear,we can get benefit from simple relaxation techniques of guaranteed convergence to the optimal solution.
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Keyword(in English) Standard regularization theory / Multi-valued functions / Transparency / Tensor product / Optimization / Surface recovery
Paper # NC93-34
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Committee NC
Conference Date 1993/9/22(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Extension of the Standard Regularization Theory into Multi-valued Functions:Recovery of Multiple Surfaces
Sub Title (in English)
Keyword(1) Standard regularization theory
Keyword(2) Multi-valued functions
Keyword(3) Transparency
Keyword(4) Tensor product
Keyword(5) Optimization
Keyword(6) Surface recovery
1st Author's Name Masahiko Shizawa
1st Author's Affiliation ATR Human Information Processing Research Laboratories()
Date 1993/9/22
Paper # NC93-34
Volume (vol) vol.93
Number (no) 247
Page pp.pp.-
#Pages 8
Date of Issue