Abstract
This paper describes an object recognition methodology called PERFORM that finds matches by establishing correspondences between model and image features using this formulation. PERFORM evaluates correspondences by intersecting error regions in the image space. The algorithm is analyzed with respect to theoretical complexity as well as actual running times. When a single solution to the matching problem is sought, the time complexity of the sequential matching algorithm for 2D-2D matching using point features is of the order O(l/sup 3/ N/sup 2/), where N is the number of model features and l is the number of image features. When line features are used, the sequential complexity is of the order O(l/sup 2/ N/sup 2/). When a single solution is sought, PERFORM runs faster than the fastest known algorithm to solve the bounded-error matching problem. The PERFORM method is shown to be easily realizable on both SIMD and MIMD architectures.
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