Tim Cootes

The University of Manchester
0000-0002-2695-9063
https://personalpages.manchester.ac.uk/staff/timothy.f.cootes/
Publications325
h-index68
Citations32,221
Highly Influential Citations2,869
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Active Shape Models-Their Training and Application
TLDR
This work describes a method for building models by learning patterns of variability from a training set of correctly annotated images that can be used for image search in an iterative refinement algorithm analogous to that employed by Active Contour Models (Snakes).
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Active Appearance Models
TLDR
A new method of matching statistical models of appearance to images by learning the relationship between perturbations in the model parameters and the induced image errors is described.
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Statistical models of appearance for computer vision
1 This is an ongoing draft of a report describing our work on Active Shape Models and Active Appearance Models. Hopefully it will be expanded to become more comprehensive, when I get the time. My…
Feature Detection and Tracking with Constrained Local Models
TLDR
This work shows that when applied to human faces, the constrained local model (CLM) algorithm is more robust and more accurate than the original AAM search method, which relies on the image reconstruction error to update the model parameters.
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Toward Automatic Simulation of Aging Effects on Face Images
TLDR
The proposed framework can be used for simulating aging effects on new face images in order to predict how an individual might look like in the future or how he/she used to look in the past and experimental results demonstrate that the performance of the face recognition system can be improved.
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The Use of Active Shape Models for Locating Structures in Medical Images
TLDR
It is described how the models can be used in local image search and give examples of their application to medical images, and how the method can be simply extended to segment 3-D objects in volume images and to track structures in image sequences.
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Training Models of Shape from Sets of Examples
TLDR
A method for building flexible shape models is presented in which a shape is represented by a set of labelled points and a number of modes of variation are determined describing the main ways in which the example shapes tend to deform from the average.
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A minimum description length approach to statistical shape modeling
TLDR
Results are given for several different training sets of two-dimensional boundaries, showing that the proposed method constructs better models than other approaches including manual landmarking-the current gold standard and can be extended straightforwardly to three dimensions.
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Use of active shape models for locating structures in medical images
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Interpreting face images using active appearance models
TLDR
A fast, robust method of interpreting face images using an Active Appearance Model (AAM), which contains a statistical model of shape and grey level appearance which can generalise to almost any face.
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