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Proceedings Paper

3D surface reconstruction from contour line image by a regularization method
Author(s): Shigeru Muraki; Naokazu Yokoya; Kazuhiko Yamamoto
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Paper Abstract

The regularization method, which is performed by minimizing an energy functional of the image, has recently been applied to many ill-posed problems in computer vision. Notably, Grimson(1983) developed a regularization method for surface reconstruction which used a sparse set of known elevation data. We have developed an approach to surface reconstruction using both contour image data and a sparse set of known elevation values. We define a new energy functional which integrates three kinds of constraints : smoothness, fitness, and contour line constraint. These constraints seek to ensure that the reconstructed surface smoothly approximates the known elevation values and has the same height value for all points on a contour line. The energy functional can be minimized by solving a large linear system of simultaneous equations. We have successfully reconstructed a detailed 3D topography by applying this method to contour lines and known sparse elevation data extracted from moire images and topographic maps.

Paper Details

Date Published: 1 August 1990
PDF: 8 pages
Proc. SPIE 1395, Close-Range Photogrammetry Meets Machine Vision, 13950U (1 August 1990); doi: 10.1117/12.2294273
Show Author Affiliations
Shigeru Muraki, Electrotechnical Lab. (Japan)
Naokazu Yokoya, Electrotechnical Lab. (Japan)
Kazuhiko Yamamoto, Electrotechnical Lab. (Japan)

Published in SPIE Proceedings Vol. 1395:
Close-Range Photogrammetry Meets Machine Vision
Armin Gruen; Emmanuel P. Baltsavias, Editor(s)

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