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Proceedings Paper

Localization of significant 3D objects in 2D images for generic vision tasks
Author(s): Marielle Mokhtari; Robert Bergevin
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Paper Abstract

Computer vision experiments are not very often linked to practical applications but rather deal with typical laboratory experiments under controlled conditions. For instance, most object recognition experiments are based on specific models used under limitative constraints. Our work proposes a general framework for rapidly locating significant 3D objects in 2D static images of medium to high complexity, as a prerequisite step to recognition and interpretation when no a priori knowledge of the contents of the scene is assumed. In this paper, a definition of generic objects is proposed, covering the structures that are implied in the image. Under this framework, it must be possible to locate generic objects and assign a significance figure to each one from any image fed to the system. The most significant structure in a given image becomes the focus of interest of the system determining subsequent tasks (like subsequent robot moves, image acquisitions and processing). A survey of existing strategies for locating 3D objects in 2D images is first presented and our approach is defined relative to these strategies. Perceptual grouping paradigms leading to the structural organization of the components of an image are at the core of our approach.

Paper Details

Date Published: 3 October 1995
PDF: 12 pages
Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); doi: 10.1117/12.222721
Show Author Affiliations
Marielle Mokhtari, Univ. Laval (Canada)
Robert Bergevin, Univ. Laval (Canada)


Published in SPIE Proceedings Vol. 2588:
Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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