Title :
A Bayesian network framework for relational shape matching
Author :
Rangarajan, Anand ; Coughlan, James ; Yuille, Alan L.
Author_Institution :
Florida Univ., Gainesville, FL, USA
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;
Conference_Titel :
Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on
Conference_Location :
Nice, France
Print_ISBN :
0-7695-1950-4
DOI :
10.1109/ICCV.2003.1238412
