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 salient features . Many different forms of model have been proposed and demonstrated [1-11].
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. We call this combination of model and search method an `Active Shape Model' (ASM). The models are generated from the statistical analysis of sets of training images, and can represent a wide variety of classes of object. Recent work has lead to a multi-resolution strategy for these Active Shape Models which allows us to locate modelled objects in 2-D images swiftly and accurately, given an initial (rough) estimate of their position. These techniques have been successfully applied to various practical problems such as face recognition , industrial inspection  and medical image analysis [1,21].
It is proposed that this fellowship be devoted to extending the theory of flexible models, to developing new forms of model and uses for such models. In addition the intention is to devise a framework to integrate the various strands of research in this area.