Genetic algorithm for affine point pattern matching
Pattern Recognition Letters
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Noisy pattern matching problems arise in many areas, e.g., computational vision, robotics, guidance and control, stereophotogrammetry, astronomy, genetics, and high-energy physics. Least-squares pattern matching over the Euclidean space En for unordered sets of cardinalities p and q is commonly formulated as a combinatorial optimization problem having complexity p(p-1)···(p-q+1), q⩽p. Since p and q may be 10 3 or larger in typical applications, less than satisfactory suboptimal methods are usually employed. A hybrid approach is described for solving the pattern matching problem under rigid motion constraints, which often apply. The method reduces the complexity to l21·n4+l12·p3, where l12 and l21 are the number of iterations required by steepest-ascent and singular value decomposition (SVD)-based procedures, respectively