From 3D scattered data to geometric signal description: invariant stable recovery of straight line segments

Patrick Hebert; Denis Laurendeau; Denis Poussart

doi:10.1117/12.141762

11 March 1993 From 3D scattered data to geometric signal description: invariant stable recovery of straight line segments

Patrick Hebert, Denis Laurendeau, Denis Poussart

Author Affiliations +

Proceedings Volume 1964, Applications of Artificial Intelligence 1993: Machine Vision and Robotics; (1993) https://doi.org/10.1117/12.141762
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

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Patrick Hebert, Denis Laurendeau, and Denis Poussart "From 3D scattered data to geometric signal description: invariant stable recovery of straight line segments", Proc. SPIE 1964, Applications of Artificial Intelligence 1993: Machine Vision and Robotics, (11 March 1993); https://doi.org/10.1117/12.141762

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