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» Learning based coarse-to-fine image registration
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IPMI
2009
Springer
14 years 18 hour ago
Dense Registration with Deformation Priors
Abstract. In this paper we propose a novel approach to define task-driven regularization constraints in deformable image registration using learned deformation priors. Our method ...
Ben Glocker, Nikos Komodakis, Nassir Navab, Georgi...
ECCV
2000
Springer
14 years 9 months ago
A Physically-Based Statistical Deformable Model for Brain Image Analysis
A probabilistic deformable model for the representation of brain structures is described. The statistically learned deformable model represents the relative location of head (skull...
Christophoros Nikou, Fabrice Heitz, Jean-Paul Arms...
ICIP
2001
IEEE
14 years 9 months ago
Use of a probabilistic shape model for non-linear registration of 3D scattered data
In this paper we address the problem of registering 3D scattered data by the mean of a statistical shape model. This model is built from a training set on which a principal compon...
Isabelle Corouge, Christian Barillot
IVC
2008
83views more  IVC 2008»
13 years 7 months ago
A minimum description length objective function for groupwise non-rigid image registration
Groupwise non-rigid registration aims to find a dense correspondence across a set of images, so that analogous structures in the images are aligned. For purely automatic inter-sub...
Stephen Marsland, Carole J. Twining, Christopher J...
MICCAI
2007
Springer
14 years 1 months ago
Robust Autonomous Model Learning from 2D and 3D Data Sets
In this paper we propose a weakly supervised learning algorithm for appearance models based on the minimum description length (MDL) principle. From a set of training images or volu...
Georg Langs, Rene Donner, Philipp Peloschek, Horst...