23 May 2013 Automatic object detection in point clouds based on knowledge guided algorithms
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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.
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Hung Truong, Hung Truong, Ashish Karmacharya, Ashish Karmacharya, Waldemar Mordwinzew, Waldemar Mordwinzew, Frank Boochs, Frank Boochs, Celeste Chudyk, Celeste Chudyk, Adlane Habed, Adlane Habed, Yvon Voisin, Yvon Voisin, "Automatic object detection in point clouds based on knowledge guided algorithms", Proc. SPIE 8791, Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection, 87910B (23 May 2013); doi: 10.1117/12.2019468; https://doi.org/10.1117/12.2019468

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