@article{ref1,
title="A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching",
journal="IEEE transactions on pattern analysis and machine intelligence",
year="2007",
author="Gavrila, Dariu M.",
volume="29",
number="8",
pages="1408-1421",
abstract="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 />",
language="",
issn="0162-8828",
doi="10.1109/TPAMI.2007.1062",
url="http://dx.doi.org/10.1109/TPAMI.2007.1062"
}