Hierarchical shape fitting using an iterated linear filter

摘要

In this paper we describe an efficient method for fitting a prior linear shape model to image data using a Kalman filter framework. This work extends previous methods in several significant respects. Firstly, the dimensionality of our shape representation is varied dynamically to reflect the available information at the current search scale so that more shape parameters are used as the fitting process converges. A coarse to fine sampling strategy is used so that the computational expense of the initial few iterations is much reduced. Finally, we re-examine the aperture problem and show how the conventional use of searching along normals to the estimated curve can be improved upon.