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Proceedings Paper

Using qualitative shape to constrain deformable model fitting
Author(s): Sven J. Dickinson; Dimitri N. Metaxas
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Paper Abstract

Recent work in qualitative shape recovery and object recognition has focused on solving the ''what is it'' problem, while avoiding the ''where is it'' problem. In contrast, typical CAD-based recognition systems have focused on the ''where is it'' problem, while assuming they know what the object is. Although each approach addresses an important aspect of the 3-D object recognition problem, each falls short in addressing the complete problem of recognizing and localizing 3-D objects from a large database. In this paper, we synthesize a new approach to shape recovery for 3-D object recognition that decouples recognition from localization by combining basic elements from these two approaches. Specifically, we use qualitative shape recovery and recognition techniques to provide strong fitting constraints on physics-based deformable model recovery techniques. On one hand, integrating qualitative knowledge of the object being fitted to the data, along with knowledge of occlusion supports a much more robust and accurate fitting. On the other hand, recovering object pose and quantitative surface shape not only provides a richer description for indexing, but supports interaction with the world when object manipulation is required. This paper presents the approach in detail and applies it to real imagery.

Paper Details

Date Published: 1 November 1992
PDF: 13 pages
Proc. SPIE 1828, Sensor Fusion V, (1 November 1992); doi: 10.1117/12.131634
Show Author Affiliations
Sven J. Dickinson, Univ. of Toronto (United States)
Dimitri N. Metaxas, Univ. of Toronto (United States)

Published in SPIE Proceedings Vol. 1828:
Sensor Fusion V
Paul S. Schenker, Editor(s)

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