Automatic model-based 3D object recognition by combining feature matching with tracking

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We propose a vision-based robust automatic 3D object recognition, which provides object identification and 3D pose information by combining feature matching with tracking. For object identification, we propose a robust visual feature and a probabilistic voting scheme. An initial object pose is estimated using correlations between the model image and the 3D CAD model, which are predefined, and the homography, byproduct of the identification. In tracking, a Lie group formalism is used for robust and fast motion computation. Experimental results show that object recognition by the proposed method improves the recognition range considerably.
Publisher
SPRINGER
Issue Date
2005-12
Language
English
Article Type
Article
Citation

MACHINE VISION AND APPLICATIONS, v.16, pp.267 - 272

ISSN
0932-8092
URI
http://hdl.handle.net/10203/20741
Appears in Collection
EE-Journal Papers(저널논문)
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