セルフキャリブレーションとNN学習によるカラーテクスチャ物体のレンダリング Image Rendering of Color Textured Object Using Self-Calibration and Neural Network Learning

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Author(s)

Abstract

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.

Journal

  • IEICE technical report

    IEICE technical report 109(249), 115-120, 2009-10-15

    The Institute of Electronics, Information and Communication Engineers

References:  6

Codes

  • NII Article ID (NAID)
    110007484295
  • NII NACSIS-CAT ID (NCID)
    AN10541106
  • Text Lang
    ENG
  • Article Type
    ART
  • ISSN
    09135685
  • NDL Article ID
    10434405
  • NDL Source Classification
    ZN33(科学技術--電気工学・電気機械工業--電子工学・電気通信)
  • NDL Call No.
    Z16-940
  • Data Source
    CJP  NDL  NII-ELS 
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