Fuzzy Associative Databases for visual recognition of 2D and 3D objects

Abstract

It is desirable for automated object recognition using computer vision systems to emulate the human capacity for recognition of shapes invariant to various transformations. We present an algorithm, based on a Fuzzy Associative Database approach, which uses appropriately invariant metrics and a neuro- fuzzy inference method to accurately classify both two- and three-dimensional objects (using different metrics for each). The system is trained using a small number of images of each object class under varying degrees of the transformations, and as we show experimentally, is then able to identify objects under other non-explicitly-trained degrees of the transformations. © 2011 TFSA.