Unstructured Object Recognition using Morphological Learning

Abstract

A technique of object recognition which can detect absence or presence of objects of interest without making explicit use of their underlying geometric structure is deemed suitable for many practical applications. In this work, a method of recognising unstructured objects has been presented, wherein several gray patterns are input as examples to a morphological rule-based learning algorithm. The output of the algorithm are the corresponding gray structuring elements capable of recognising patterns in query images. The learning is carried out offline before recognition of the queries. The technique has been tested to identify fuel pellet surface imperfections. Robustness wrt intensity, orientation, and shape variations of the query patterns is built into the method. Moreover, simplicity of the recognition process leading to reduced computational time makes the method attractive to solve many practical problems.