Presentation 2009-10-23
Image Rendering of Color Textured Object Using Self-Calibration and Neural Network Learning
Yi DING, Yuji IWAHORI, Tsuyoshi NAKAMURA, Lifeng HE, Hidenori ITOH,
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Abstract(in English) This paper describes a new approach for self-calibration and color image rendering using radial basis function (RBF) neural network. Most empirical approaches make use of a calibration object. Here, we require no calibration object to both shape recovery and color image rendering. The neural network learning data are obtained through the rotations of a target object. The approach can generate realistic virtual images without any calibration object which has the same reflectance properties as the target object. The proposed approach uses a neural network to obtain both surface orientation and albedo, and applies another neural network to generate virtual images from any viewpoint under any direction of light source. Experiments with real data are demonstrated.
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Keyword(in English) Neural Network Based Rendering / Photometric Stereo / Self-Calibration / Albedo / Shape Recovery
Paper # PRMU2009-91
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Conference Information
Committee PRMU
Conference Date 2009/10/15(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Image Rendering of Color Textured Object Using Self-Calibration and Neural Network Learning
Sub Title (in English)
Keyword(1) Neural Network Based Rendering
Keyword(2) Photometric Stereo
Keyword(3) Self-Calibration
Keyword(4) Albedo
Keyword(5) Shape Recovery
1st Author's Name Yi DING
1st Author's Affiliation Nagoya Institute of Technology()
2nd Author's Name Yuji IWAHORI
2nd Author's Affiliation Chubu University
3rd Author's Name Tsuyoshi NAKAMURA
3rd Author's Affiliation Nagoya Institute of Technology
4th Author's Name Lifeng HE
4th Author's Affiliation Aichi Prefectural University
5th Author's Name Hidenori ITOH
5th Author's Affiliation Nagoya Institute of Technology
Date 2009-10-23
Paper # PRMU2009-91
Volume (vol) vol.109
Number (no) 249
Page pp.pp.-
#Pages 6
Date of Issue