Group Actions, Homeomorphisms, and Matching: A General Framework
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Brownian Warps: A Least Committed Prior for Non-rigid Registration
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Computing Optic Flow by Scale-Space Integration of Normal Flow
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
A Statistical Approach to Large Deformation Diffeomorphisms
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
The Generic Structure of the Optic Flow Field
Journal of Mathematical Imaging and Vision
Landmark matching via large deformation diffeomorphisms
IEEE Transactions on Image Processing
Guest Editorial: Generative model based vision
Computer Vision and Image Understanding
On the computational rationale for generative models
Computer Vision and Image Understanding
Brownian Warps for Non-Rigid Registration
Journal of Mathematical Imaging and Vision
Guessing Tangents in Normal Flows
Journal of Mathematical Imaging and Vision
Statistical M-Estimation and Consistency in Large Deformable Models for Image Warping
Journal of Mathematical Imaging and Vision
TV-L1 optical flow for vector valued images
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
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Using standard statistical assumptions we derive a stochastic differential equation generating flows of diffeomorphisms. These stochastic processes provide a generative model for non-rigid registration and image warping problems. We give a mathematically rigorous derivation of the renormalized Brownian density in context of maximum a posteriori estimation of the underlying Brownian motions driving the warp flow. The second part of the paper combines the prior model with a likelihood model for image sequences. The combined model is employed to study the warp field for an image sequence of turbulent smoke.