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

George K. Knopf; Jonathan Kofman

doi:10.1117/12.256268

29 October 1996 Neural network mapping of image-to-object coordinates for 3D shape reconstruction

George K. Knopf, Jonathan Kofman

Proceedings Volume 2904, Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling; (1996) https://doi.org/10.1117/12.256268
Event: Photonics East '96, 1996, Boston, MA, United States

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.

Citation Download Citation

George K. Knopf and Jonathan Kofman "Neural network mapping of image-to-object coordinates for 3D shape reconstruction", Proc. SPIE 2904, Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling, (29 October 1996); https://doi.org/10.1117/12.256268

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