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Proceedings Paper

Neural network mapping of image-to-object coordinates for 3D shape reconstruction
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Paper Abstract

A neural network approach that automatically maps measured 2D image coordinates to 3D object coordinates for shape reconstruction is described. The appropriately trained radial-basis function network eliminates the need for rigorous calibration procedures. The training and test data are obtained by capturing successive images of the intersection points between a projected light line and horizontal strips on a calibration bar. Once trained, the 3D object space coordinates that correspond to an illuminated pixel in the image plane is determined from the neural network. In addition, the generalization capabilities of the neural network enable the intermediate points to be interpolated. An experimental study is presented in order to demonstrate the effectiveness of this approach to 3D measurement and reconstruction.

Paper Details

Date Published: 29 October 1996
PDF: 9 pages
Proc. SPIE 2904, Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling, (29 October 1996); doi: 10.1117/12.256268
Show Author Affiliations
George K. Knopf, Univ. of Western Ontario (Canada)
Jonathan Kofman, Univ. of Western Ontario (Canada)


Published in SPIE Proceedings Vol. 2904:
Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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