The Community for Technology Leaders
Green Image
Histograms of shape signature or prototypical shapes, called shapemes, have been used effectively in previous work for 2D/3D shape matching and recognition. We extend the idea of shapeme histogram to recognize partially observed query objects from a database of complete model objects. We propose representing each model object as a collection of shapeme histograms and match the query histogram to this representation in two steps: 1) compute a constrained projection of the query histogram onto the subspace spanned by all the shapeme histograms of the model and 2) compute a match measure between the query histogram and the projection. The first step is formulated as a constrained optimization problem that is solved by a sampling algorithm. The second step is formulated under a Bayesian framework, where an implicit feature selection process is conducted to improve the discrimination capability of shapeme histograms. Results of matching partially viewed range objects with a 243 model database demonstrate better performance than the original shapeme histogram matching algorithm and other approaches.
Shapeme histogram, spin image, Gibbs sampling, feature saliency, object recognition, Bayesian analysis.
Bogdan Matei, Harpreet S. Sawhney, Rakesh Kumar, Ying Shan, "Shapeme Histogram Projection and Matching for Partial Object Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 568-577, April 2006, doi:10.1109/TPAMI.2006.83
165 ms
(Ver 3.3 (11022016))