Global image registration based on learning the prior appearance model

A new approach to align an image of a textured object with a given prototype (learned reference object) is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity to the prototype (learned reference object) is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. To get accurate appearance model, we developed a new approach to automatically select the most important cliques (neighborhood system) that describe the visual appearance of a texture object. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.