A survey of image registration techniques
ACM Computing Surveys (CSUR)
Curvature Based Image Registration
Journal of Mathematical Imaging and Vision
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Mass Preserving Mappings and Image Registration
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Non-Rigid Matching Using Demons
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
CBMS '95 Proceedings of the Eighth Annual IEEE Symposium on Computer-Based Medical Systems
A New Class of Elastic Body Splines for Nonrigid Registration of Medical Images
Journal of Mathematical Imaging and Vision
A Robust Algorithm for Point Set Registration Using Mixture of Gaussians
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
The Asymmetry of Image Registration and Its Application to Face Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete quadratic curvature energies
Computer Aided Geometric Design
Incorporating rigid structures in non-rigid registration using triangular b-splines
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
Voxel-based 2-D/3-D registration of fluoroscopy images and CT scans for image-guided surgery
IEEE Transactions on Information Technology in Biomedicine
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We introduce a novel nonrigid 2D image registration method that establishes dense and accurate correspondences across images without the need of any manual intervention. Our key insight is to model the image as a membrane, i.e., a thin 3D surface, and to constrain its deformation based on its geometric properties. To do so, we derive a novel Bayesian formulation. We impose priors on the moving membrane which act to preserve its shape as it deforms to meet the target.We derive these as curvature weighted first and second order derivatives that correspond to the changes in stretching and bending potential energies of the membrane and estimate the registration as the maximum a posteriori. Experimental results on real data demonstrate the effectiveness of our method, in particular, its robustness to local minima and its ability to establish accurate correspondences across the entire image. The results clearly show that our method overcomes the shortcomings of previous intensity-based and feature-based approaches with conventional uniform smoothing or diffeomorphic constraints that suffer from large errors in textureless regions and in areas in-between specified features.