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. 1 Introduction The Kalman filter  has proven a useful tool for real-time tracking in computer vision [2, 3]. Much of this work concentrates on the problem of tracking one or more objects through a sequence of images. Blake et al  set out a mathematical framework for tracking contours represent..