Dynamic Feature Ordering for Efficient Registration

Existing sequential feature-based registration algorithms involving search typically either select features randomly (eg. the RANSAC[8] approach) or assume a predefined, intuitive ordering for the features (eg. based on size or resolution). This paper presents a formal framework for computing an ordering for features which maximizes search efficiency. Features are ranked according to matching ambiguity measure, and an algorithm is proposed which couples the feature selection with the parameter estimation, resulting in a dynamic feature ordering. The analysis is extended to template features where the matching is non-discrete and a sample-refinement process is proposed. The framework is demonstrated effectively on the localization of a person in an image, using a kinematic model with template features. Different priors are used on the model parameters and the results demonstrate nontrivial variations in the optimal feature hierarchy.