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A cross-correlational algorithm for determining image correspondences is described. As is typical for such algorithms, correct determination of correspondences at a fairly high level of accuracy is relatively easy to achieve. The problem addressed in the algorithm is the development of consistency checking mechanisms that enable the detection and correction of the remaining relatively small number of incorrect match and non-match determinations. The image matching algorithm and its proposed consistency checking mechanism differ from more traditional relaxation-based methods in two ways: 1. the algorithm takes a more "committed" approach in terms of actively making discrete inferences about match and non-match hypotheses based on relatively local support, even at the earliest stages of the algorithm, and 2. the consistency checking mechanisms permit the detection and isolation of sets of match and non-match hypotheses that are inconsistent with neighboring hypotheses. Within the context of graph matching - with nodes being detected image features and edges the spatial relationship between features - the algorithm "commits" itself by creating "virtual" nodes, which function as hypotheses about where missing features should appear. The criterion for consistency embodies the notion that the result of the comparison process should consist of match information which represents the construction of the union out of the initial two graphs being compared - in contrast to the continuity criterion embodied in relaxation methods. The comparison algorithm should be of interest in areas of application where image differences are reconcilable, e.g., when missing features on one image may not have been detected correctly initially, but might be found via more focused processing. Model-based matching may be a candidate application. Preliminary results are presented on the application of this consistency methodology to the comparison of biological images of two-dimensional electrophoretic gels used in the detection of mutations.
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