A Bayesian network framework for relational shape matching

Author

Rangarajan, Anand ; Coughlan, James ; Yuille, Alan L.

Author_Institution

Florida Univ., Gainesville, FL, USA

fYear

2003

fDate

13-16 Oct. 2003

Firstpage

671

Abstract

A Bayesian network formulation for relational shape matching is presented. The main advantage of the relational shape matching approach is the obviation of the nonrigid spatial mappings used by recent nonrigid matching approaches. The basic variables that need to be estimated in the relational shape matching objective function are the global rotation and scale and the local displacements and correspondences. The new Bethe free energy approach is used to estimate the pairwise correspondences between links of the template graphs and the data. The resulting framework is useful in both registration and recognition contexts. Results are shown on hand-drawn templates and on 2D transverse T1-weighted MR images.

Keywords

belief networks; graph theory; image matching; image registration; magnetic resonance imaging; 2D transverse T1-weighted MR image; Bayesian network formulation; Bethe free energy approach; image correspondence estimation; image recognition; image registration; nonrigid spatial mapping; objective function; relational shape matching; template graph; Bayesian methods; Biomedical computing; Biomedical imaging; Image segmentation; Indexing; Object recognition; Optical imaging; Shape; Spline; Topology;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on

Conference_Location

Nice, France

Print_ISBN

0-7695-1950-4

Type

conf

DOI

10.1109/ICCV.2003.1238412

Filename

1238412