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Proceedings Paper

From 3D scattered data to geometric signal description: invariant stable recovery of straight line segments
Author(s): Patrick Hebert; Denis Laurendeau; Denis Poussart
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Paper Abstract

Many tasks, such as pose determination, object recognition, and model building rely on a geometrical description of the visible surface derived from 3-D scattered measurements. Even though much effort has been invested in surface description, little attention has been paid to the invariant recovery of geometric information from an actual noisy 3-D signal. In this work, we argue that the local description of a section of a visible surface must be stable with the measurement set gathered from any of the different viewpoints in the scene. Stability can be achieved on sections where constraints are redundant with respect to a polynomial model. A segmentation approach is developed to identify such stable sections. The approach is based on a measurement error model which takes into account the sensor's viewpoint. The case of straight line extraction from 3-D single scan profiles of a surface is presented. Identified stable linear sections are stored in a graph that includes the estimated descriptive parameters for each section and indices of reliability for each description.

Paper Details

Date Published: 11 March 1993
PDF: 12 pages
Proc. SPIE 1964, Applications of Artificial Intelligence 1993: Machine Vision and Robotics, (11 March 1993);
Show Author Affiliations
Patrick Hebert, Univ. Laval (Canada)
Denis Laurendeau, Univ. Laval (Canada)
Denis Poussart, Univ. Laval (Canada)

Published in SPIE Proceedings Vol. 1964:
Applications of Artificial Intelligence 1993: Machine Vision and Robotics
Kim L. Boyer; Louise Stark, Editor(s)

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