Flexible Models for Computer Vision


Over recent years flexible models have become increasingly important for the interpretation of images in computer vision. They are used to represent both the shape and grey-level appearance of image structures. They can be used to locate examples of structures in new images, to classify objects found in images and to filter images to pick out interesting features.

We have developed both flexible models of the shape and appearance of variable image structures. We have also developed search techniques which allow such structures to be located in new images. The models are generated from the statistical analysis of sets of training images, and can represent a wide variety of classes of object.

These techniques have been successfully applied to various practical problems such as face recognition, industrial inspection and medical image analysis.

Much of my research is devoted to extending the theory of flexible models, to developing new forms of model and applying the models to new problem areas.

Brief overviews:
1) Statistical Shape Models.
2) Active Shape Models.
3) Combined Appearance Models.
4) Active Appearance Models.
5) View-Based Appearance Models.
6) Tracking with View-Based Appearance Models.
A detailed report about Active Shape Models and Active Appearance Models Postscript (>1.6Mb)
Tim Cootes