Optimal estimation applied to visual contour tracking

This paper derives an optimal estimator for the purpose of online visual contour tracking. Starting from Bayesian segmentation as the measurement strategy, we use a bottom-up approach to design the estimator. In particular, it is shown that additive imaging noise leads to multiplicative segmentation uncertainty from which a geometric averaging update model is established. Given known noise statistics, the optimal correction gain and associated filtering equations are derived. The optimal estimator is applied to noise-corrupted imagery and its performance compared against a fixed-gain filtering strategy and other visual tracking techniques.