Contour tracking based on marginalized likelihood ratios

When fitting contour models to image data, it is necessary to take into account unmodelled shape variability. Traditionally, this has been done either by blurring the input image or by looking for image features in the neighborhood of the contour. A more statistically rigorous approach is to marginalize over all possible shape deformations. When this is done, the resulting likelihood model has similarities to both the blurring approach and the feature-based approach.A tracking application is used to demonstrate the marginalized likelihood model and compare it to the blurring approach. The best tracking results were obtained with the new model when combined with the Expectation–Maximization (EM) algorithm.