Model Based Object Recognition by Robust Information Fusion
Haifeng Chen, Ilan Shimshoni(*) and Peter Meer
Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
(*) Industrial Engineering and Management Department
Technion, Haifa 32000, Israel
Given a set of 3D model features and their 2D image, model based
object recognition determines the correspondences between those features and hence
computes the pose of the object.
To achieve good recognition results, a novel approach based on robust
information fusion is put forward in this paper.
In this algorithm, the property of probabilistic peaking effect is
employed to generate sets of hypothesized matches between model and image points.
The correct hypotheses are obtained by searching for clusters among
projections of predefined 3D reference points
using the pose implied by each hypothesis.
To assure the robustness of clustering, a new data fusion technique
that is based on the nonparametric mode search method, mean shift, is proposed.
The uncertainty information of the hypotheses is also incorporated into the
fusion process
to adaptively determine the bandwidth of the mean shift procedure.
Experimental
results demonstrating the satisfactory performance of this algorithm
are presented.
17th International Conference on Pattern Recognition
, Cambridge, U.K., August 2004, III:57-60.
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