TY  - JOUR
PY  - 2007//
TI  - A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
JO  - IEEE transactions on pattern analysis and machine intelligence
A1  - Gavrila, Dariu M.
SP  - 1408
EP  - 1421
VL  - 29
IS  - 8
N2  - This paper presents a novel probabilistic approach to hierarchical, exemplar-based shape matching. No feature correspondence is needed among exemplars, just a suitable pairwise similarity measure. The approach uses a template tree to efficiently represent and match the variety of shape exemplars. The tree is generated offline by a bottom-up clustering approach using stochastic optimization. Online matching involves a simultaneous coarse-to-fine approach over the template tree and over the transformation parameters. The main contribution of this paper is a Bayesian model to estimate the a posteriori probability of the object class, after a certain match at a node of the tree. This model takes into account object scale and saliency and allows for a principled setting of the matching thresholds such that unpromising paths in the tree traversal process are eliminated early on. The proposed approach was tested in a variety of application domains. Here, results are presented on one of the more challenging domains: real-time pedestrian detection from a moving vehicle. A significant speed-up is obtained when comparing the proposed probabilistic matching approach with a manually tuned nonprobabilistic variant, both utilizing the same template tree structure.<p /> 
LA  - 
SN  - 0162-8828
UR  - http://dx.doi.org/10.1109/TPAMI.2007.1062
ID  - ref1
ER  -