On The Congruence Of Noisy Images To Line Segment Models

An algorithm is described for matching two-dimensional images that does not depend on the extraction of features, a step which is often difficult or impractical. It combines computational efficiency with robustness against noisy data. It has been tested with two kinds of data. The algorithm matches a two-dimensional image, in the form of a set of points, to a line segment model. It estimates a positive congruence (i.e., a translation plus a rotation) that moves the image onto the model. The algorithm is most appropriate for matching when the required congruence is small, and it seems very well suited to motion-tracking. Experimental results have been obtained for two kinds of images: (1) points obtained from an optical range finder, and (2) edge points extracted from an intensity image. Only the former results are presented.