Rigid body constrained noisy point pattern matching

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 E/sup n/ for unordered sets of cardinalities p and q is commonly formulated as a combinatorial optimization problem having complexity p(p-1)/spl middot//spl middot//spl middot/(p-q+1), q/spl les/p. Since p and q may be 10/sup 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 l/sub 21//spl middot/n/sup 4/+l/sub 12//spl middot/p/sup 3/, where l/sub 12/ and l/sub 21/ are the number of iterations required by steepest-ascent and singular value decomposition (SVD)-based procedures, respectively.<
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