MFT based discrete relaxation for matching high order relational structures

Abstract

This paper presents a new relaxation labelling approach for matching image structures characterized by high order relations. A Markov random field (MRF) is employed to represent the prior contextual information. The consistent labelling is defined as the maximum a posteriori (MAP) labelling. It is achieved using iterative updating according to a rule derived using mean field theory (MFT). The benefits of the approach include the embedding of observations into the matching criterion function and the ability of the algorithm to find the global rather than nearest local optimum. The approach is applied to stereo vision and the experimental results demonstrate its viability.