29 May 2007 Dynamic replanning of 3D automated reconstruction using situation graph trees and illumination adjustment
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Proceedings Volume 6356, Eighth International Conference on Quality Control by Artificial Vision; 635602 (2007) https://doi.org/10.1117/12.736684
Event: Eighth International Conference on Quality Control by Artificial Vision, 2007, Le Creusot, France
Abstract
This paper presents an original approach for the optimal 3D reconstruction of manufactured workpieces based on a priori planification of the task, enhanced on-line through dynamic adjustment of the lighting conditions, and built around a cognitive intelligent sensory system using so-called Situation Graph Trees. The system takes explicitely structural knowledge related to image acquisition conditions, type of illumination sources, contents of the scene (e. g., CAD models and tolerance information), etc. into account. The principle of the approach relies on two steps. First, a socalled initialization phase, leading to the a priori task plan, collects this structural knowledge. This knowledge is conveniently encoded, as a sub-part, in the Situation Graph Tree building the backbone of the planning system specifying exhaustively the behavior of the application. Second, the image is iteratively evaluated under the control of this Situation Graph Tree. The information describing the quality of the piece to analyze is thus extracted and further exploited for, e. g., inspection tasks. Lastly, the approach enables dynamic adjustment of the Situation Graph Tree, enabling the system to adjust itself to the actual application run-time conditions, thus providing the system with a self-learning capability.
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Sophie Kohler, Sophie Kohler, Aïcha Beya Far, Aïcha Beya Far, Ernest Hirsch, Ernest Hirsch, } "Dynamic replanning of 3D automated reconstruction using situation graph trees and illumination adjustment", Proc. SPIE 6356, Eighth International Conference on Quality Control by Artificial Vision, 635602 (29 May 2007); doi: 10.1117/12.736684; https://doi.org/10.1117/12.736684
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