Two-Dimensional Shape Recognition Using Redundant Hashing

Abstract

In addition to invariance with respect to certain geometric transformations, there are two other key requirements for any shape recognition system. It should be flexible enough to adopt to a variety of sets of shapes with minimal training; and it should be capable of performing even in presence of occlusion. This paper describes one such shape recognition system that is currently under development. The system is based on the redundant hashing scheme of Kohonen for recognizing and correcting misspelt words. The current version of the system is meant for 2 -D shapes; however, the same approach is applicable to 3 -D shape recognition. In the present implementation for 2 -D shapes, a polygonal approximation of the given shape is encoded in the form of a string. The encoded string is then used to generate a very small set of shape hypotheses through the use of redundant hashing. The best hypothesis from the set of competing hypotheses is selected by a very simple matching scheme followed by a verification phase based on rotation transformation. The experiments thus far indicate that the system is capable of recognizing shapes in presence of occlusion with about 5% error. The system has significant ability to adopt to new sets of shapes ; it does not require any training except the building of hash index table and the corresponding shape dictionary.