A framework for interpreting noisy, two-dimensional images, based on a fuzzification of programmed, attributed graph grammars

Abstract

This thesis investigates a fuzzy syntactic approach to the interpretation of noisy two-dimensional images. This approach is based on a modification of the attributed graph grammar formalism to utilise fuzzy membership functions in the applicability predicates. As far as we are aware, this represents the first such modification of graph grammars. Furthermore, we develop a method for programming the resultant fuzzy attributed graph grammars through the use of non-deterministic control diagrams. To do this, we modify the standard programming mechanism to allow it to cope with the fuzzy certainty values associated with productions in our grammar. Our objective was to develop a flexible framework which can be used for the recognition of a wide variety of image classes, and which is adept at dealing with noise in these images. Programmed graph grammars are specifically chosen for the ease with which they allow one to specify a new two-dimensional image class. We implement a prototype system for Optical Music Recognition using our framework. This system allows us to test the capabilities of the framework for coping with noise in the context of handwritten music score recognition. Preliminary results from the prototype system show that the framework copes well with noisy images.