Active shape models—their training and application
Computer Vision and Image Understanding
3D Statistical Shape Models Using Direct Optimisation of Description Length
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Automatic Construction of Active Appearance Models as an Image Coding Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Representation and Detection of Deformable Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Robust autonomous model learning from 2D and 3D data sets
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Automatic learning sparse correspondences for initialising groupwise registration
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Deformable object modelling and matching
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Automatic part selection for groupwise registration
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
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We address the problem of building detailed models of the shape and appearance of complex structures, given only a training set of representative images and some minimal manual intervention. We focus on objects with repeating structures (such as bones in the hands), which can cause normal deformable registration techniques to fall into local minima and fail. Using a sparse annotation of a single image we can construct a parts+geometry model capable of locating a small set of features on every training image. Iterative refinement leads to a model which can locate structures accurately and reliably. The resulting sparse annotations are sufficient to initialise a dense groupwise registration algorithm, which gives a detailed correspondence between all images in the set. We demonstrate the method on a large set of radiographs of the hand, achieving submillimeter accuracy.