Associating an image by network constraint analysis

A technique for associating an image is described in terms of the network constraint analysis. Pixels and their gray-values are called units and labels, respectively, and a set of n pixels called the unit constraint set T provides the unit-label constraint set R for memorized images. Given an incomplete image X which can have occlusion, noise, distortion, etc., R receives screening by X to yield the reduced set R* (R* contained in R), since the elements of X constrain the elements in R. A depth first search is then applied to the elements of R* to obtain consistent solutions, if any, which are associated images. The proposed technique is free from the interference among memorized images which other association techniques suffer from. An iterative technique is also proposed for speeding up the depth first search. Performance of the proposed association technique is shown by the experiment employing 26 alphabetical letters. >