Timothy F. Cootes
University of Manchester
329 Papers
4.6K Citations
Timothy F. Cootes is an academic researcher from University of Manchester. The author has contributed to research in topics: Active appearance model & Active shape model. The author has an hindex of 70, co-authored 306 publications. Previous affiliations of Timothy F. Cootes include RMIT University & Victoria University of Manchester.
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Papers
Registering richly labelled 3D images
K. O. Babalola,Timothy F. Cootes +1 more
- 06 Apr 2006
TL;DR: The method of registering 3D images in which many regions have been segmented and labelled is demonstrated by using it to construct statistical shape models by applying a groupwise alignment method to a set of richly labelled 3D brain images.
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Determining correspondences for statistical models of facial appearance
Kevin Walker,Timothy F. Cootes,Christopher J. Taylor +2 more
- 26 Mar 2000
TL;DR: This work presents an iterative scheme in which pairwise correspondences are used to determine a global correspondence across the entire set of images, and demonstrates that an appearance model trained on the correspondences is of higher quality than one built from hand-marked images.
Simultaneous registration, segmentation and modelling of structure in groups of medical images
Vladimir Petrovic,Timothy F. Cootes,Carole J. Twining,Christopher J. Taylor +3 more
- 12 Apr 2007
TL;DR: An algorithm which simultaneously segments and registers a set of medical images, incrementally constructing a model of their structure and the correspondences across the set, to address the problem of extracting information from groups ofmedical images of the same anatomy is described.
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Automatic model matching using part based model constrained active appearance models for skeletal maturity
Steve A. Adeshina,Timothy F. Cootes +1 more
- 01 Sep 2015
TL;DR: This work used part-based models to initialize the image of an incoming radiographic image and then fit a global Active Appearance models of the whole hand using the found points from the Part based models as `weighted' constraints to refine the model fit.
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Classification of Osteoporotic Vertebral Fractures Using Shape and Appearance Modelling
Paul A. Bromiley,Eleni P. Kariki,Judith E. Adams,Timothy F. Cootes +3 more
- 10 Sep 2017
TL;DR: It is demonstrated that the combination of RF classifiers and appearance modelling results in a significant (up to 60% reduction in false positive rate at 80% sensitivity) improvement in diagnostic accuracy.