A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching

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. Th...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2007-08, Vol.29 (8), p.1408-1421
Main Author: Gavrila, D.M.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Bayesian analysis
Bayesian methods
Bayesian models
chamfer distance
Computer science
control theory
systems
Exact sciences and technology
Hierarchical shape matching
Image segmentation
Intelligence
Matching
Pattern analysis
Pattern recognition. Digital image processing. Computational geometry
Probabilistic methods
Probability theory
Prototypes
Robustness
Shape measurement
Similarity
Stochastic processes
Studies
Testing
Tree data structures
Trees
Vehicle detection
Vehicles
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Summary: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.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2007.1062