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Proceedings Paper

Grid Labeling Using A Marked Grid
Author(s): Stanley M. Dunn; Richard L. Keizer
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Paper Abstract

Accurate grid labeling is a key step in recovering 3-D surfaces from structured light images. Knowledge of the real world (projector and camera geometry, surface continuity and smoothness, etc.) can be used to derive a set of local and global constraints which the grid labels must satisfy. Propagation of these constraints eliminates all but a small set of possible grid labels, but ambiguous solutions may still remain. This paper discusses a method of eliminating grid labeling ambiguity by adding constraints introduced by placing markers within the grid pattern. Grid labeling is based on geometric and topological constraints. Geometric constraints are global constraints on grid labels arising from knowledge of the camera and projector geometry, from assumed opaqueness of the object, and from knowledge of the work volume. Topological constraints are local constraints on grid labels arising from the sequential ordering of grid labels along a single grid stripe in the camera image, and from the assumption that a continuous (smooth) network of grid stripes in the camera image indicates a continuous (smooth) three-dimensional surface. This last assumption may be sometimes violated due to infrequent "viewing accidents" which may be caused by surface irregularities such as occluding contours or creases or by image processing errors. A problem with previous methods is the possible ambiguity of the recovered surface. This ambiguity occurs when more than one globally consistent set of grid labels is obtained, and consequently more than one object surface is possible. Our results show that the locations of the grid markers provide additional constraints to guide the grid labeling. We will present results of using several algorithms for labeling grids in structured light images. We will show that the additional constraints can be easily included into constraint propagation algorithms previously used for grid labeling.

Paper Details

Date Published: 7 March 1989
PDF: 6 pages
Proc. SPIE 1005, Optics, Illumination, and Image Sensing for Machine Vision III, (7 March 1989); doi: 10.1117/12.949034
Show Author Affiliations
Stanley M. Dunn, Rutgers University (United States)
Richard L. Keizer, Rutgers University (United States)

Published in SPIE Proceedings Vol. 1005:
Optics, Illumination, and Image Sensing for Machine Vision III
Donald J. Svetkoff, Editor(s)

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