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DAGM
1997
Springer

A Feature Map Approach to Real-Time 3-D Object Pose Estimation from Single 2-D Perspective Views

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A Feature Map Approach to Real-Time 3-D Object Pose Estimation from Single 2-D Perspective Views
A novel approach to the computation of an approximate estimate of spatial object pose from camera images is proposed. The method is based on a neural network that generates pose hypotheses in real time, which can be refined by registration or tracking systems. A modification of Kohonen’s self-organizing feature map is systematically trained with computer generated object views such that it responds to a preprocessed image with one or more sets of object orientation parameters. The key concepts proposed are representations of spatial orientation that result in continuous distance measures, and the choice of a fixed network topology that is best suited to the representation of 3-D orientation. Experimental results from both simulated and real images demonstrate that a pose estimate within the accuracy requirements can be found in more than 90% of all cases. The current implementation operates at near frame rate on real world images.
S. Winkler, Patrick Wunsch, Gerd Hirzinger
Added 07 Aug 2010
Updated 07 Aug 2010
Type Conference
Year 1997
Where DAGM
Authors S. Winkler, Patrick Wunsch, Gerd Hirzinger
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