Abstract

We propose an algorithm for object recognition based on clustering of vectors in the space of coefficients of affine transformations obtained as a result of the formation of hypotheses about the correspondence of segments of contours of a reference image and an input image approximated by linear segments. The results of numerical studies using a collection of images from New York University show that the proposed algorithm has a higher efficiency than an algorithm based on invariant moments or an algorithm for the invariant-to-scale comparison of singular points.

© 2017 Optical Society of America

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