Deterministic sample consensus with multiple match hypotheses (with Paul McIlroy, Ed Rosten and Simon Taylor)

This paper proposes a deterministic scheme for selecting correspondences from feature matching to generate motion hypotheses. The method combines matching scores, ambiguity and the past performance of motion hypotheses generated by the matches, to estimate the probability that a feature match is correct. At every stage the best correspondences are chosen to generate a hypothesis. This method will therefore only spend time on poor or ambiguous matches when the best correspondences have proven themselves to be unsuitable. The result is a system that is able to operate efficiently on ambiguous data and is suitable for implementation on devices with limited computing resources. 
[2010 BMVC Paper]