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Proceedings Paper

Automatic object detection in point clouds based on knowledge guided algorithms
Author(s): Hung Truong; Ashish Karmacharya; Waldemar Mordwinzew; Frank Boochs; Celeste Chudyk; Adlane Habed; Yvon Voisin
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Paper Abstract

The modeling of real-world scenarios through capturing 3D digital data has been proven applicable in a variety of industrial applications, ranging from security, to robotics and to fields in the medical sciences. These different scenarios, along with variable conditions, present a challenge in discovering flexible appropriate solutions. In this paper, we present a novel approach based on a human cognition model to guide processing. Our method turns traditional data-driven processing into a new strategy based on a semantic knowledge system. Robust and adaptive methods for object extraction and identification are modeled in a knowledge domain, which has been created by purely numerical strategies. The goal of the present work is to select and guide algorithms following adaptive and intelligent manners for detecting objects in point clouds. Results show that our approach succeeded in identifying the objects of interest while using various data types.

Paper Details

Date Published: 23 May 2013
PDF: 8 pages
Proc. SPIE 8791, Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection, 87910B (23 May 2013); doi: 10.1117/12.2019468
Show Author Affiliations
Hung Truong, i3mainz (Germany)
Le2i, CNRS, Univ. of Burgundy (France)
Ashish Karmacharya, i3mainz (Germany)
Waldemar Mordwinzew, i3mainz (Germany)
Frank Boochs, i3mainz (Germany)
Celeste Chudyk, i3mainz (Germany)
Adlane Habed, Le2i, CNRS, Univ. of Burgundy (France)
Yvon Voisin, Le2i, CNRS, Univ. of Burgundy (France)


Published in SPIE Proceedings Vol. 8791:
Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection
Fabio Remondino; Mark R. Shortis; Jürgen Beyerer; Fernando Puente León, Editor(s)

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