Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Deformable Shapes Using Loopy Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Network Framework for Relational Shape Matching
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Parameter Estimation for MRF Stereo
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Robust 3D Shape Correspondence in the Spectral Domain
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
A Shape Representation for Planar Curves by Shape Signature Harmonic Embedding
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Shape registration by simultaneously optimizing representation and transformation
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Deformable density matching for 3D non-rigid registration of shapes
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
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This paper addresses the problem of fully automatic matching two triangulated surface meshes. In this paper, a similarity measurement is constructed to measure the consistency of the constraints among the correspondent landmarks, which is rigid transformation immune and robust to nonrigid deformations. The matching problem is then solved by directly finding correspondence between the landmarks of the two surfaces by graphical model based Bayesian inference. In order to reduce the computational complexity and to accelerate the convergence, a hierarchical graphical model is constructed which enables mutual registration and information exchange between the two surfaces during registration. Experiments on randomly generated instances from a PCA based statistical model of proximal femurs verified the proposed approach.