8 July 1994 Point set pattern matching using the Procrustean metric
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Abstract
A fundamental problem in computer vision is to determine if an approximate version of a geometric pattern P occurs in an observed set of points B. The pattern and the background are modeled as point sets Pequals{p$1,....,p$m} and Bequals{b$1,....,b$n} on the line or in the plane. We wish to find a transformation T, from a family of transformations , such that the distance between T(P) and B is minimized. The distance between T(P) and B is the sum of the distances squared between T(p$i) and the closest point in B. This is the Procrustean metric where the set of allowable mappings between P and B is the space F of all functions from P into B. The algorithms in this paper also apply when the metric is the sum of the distances between points in P and B. We present algorithms that minimize the Procrustean metric for the following families of transformations: translations in R1, translations in R2, and combined translations and rotations in R2. We prove that fixed point algorithms for computing the Procrustean metric converge to a fixed point and show a worst case lower bound on the number of fixed points.
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Jonathan Phillips, "Point set pattern matching using the Procrustean metric", Proc. SPIE 2299, Mathematical Methods in Medical Imaging III, (8 July 1994); doi: 10.1117/12.179249; https://doi.org/10.1117/12.179249
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KEYWORDS
Computer vision technology

Detection and tracking algorithms

Machine vision

Algorithm development

Algorithms

Promethium

Fourier transforms

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