Active Shape Models: Evaluation of a Multi-Resolution Method for Improving Image Search

Abstract

We describe a multi-resolution technique for locating for variable structures in images. This is an extension of work on Active Shape Models (ASMs) - statistical models which iteratively deform to match image data. An ASM consists of a shape model controlling a set of landmark points, together with a statistical model of the grey-levels expected around each landmark. Both the shape model and the grey-level models are trained on sets of labelled example images. In order to apply a coarse-to-fine search strategy it is necessary to train a set of grey-level models for each landmark, one for every level of a multi-resolution image pyramid. During image search the model is started on the coarsest resolution image. As the search progresses it moves to finer and finer resolutions until no further improvement can be made. We describe an automatic technique for deciding when to :ss has converged. We demonstrate the ntitative experiments which show a sigi speed and quality of fit compared to previous methods.