Cellular associative neural networks for image interpretation
Christos Orovas · 1997
This paper describes the architecture and the operation of a neural network based system for image interpretation. The system is based on the use of two models of associative neural networks, ADAM and AURA for image and symbolic processing respectively. Employing characteristics of cellular automata theory and applying ideas from syntactic and structural pattern recognition, it uses a hierarchical approach to learn the structure of images. The hardware implementation of this system is based on the C-NNAP hardware platform. INTRODUCTION The structure of patterns found in images and the relationships among the primitive elements of these patterns are of a great importance in any image understanding system. Structure handling systems are following the syntactic and structural approach for pattern analysis (Fu (1), Bunke and Sunfeliu (2)). Although successfully applied at a number of cases, the sensitivity of parsing and graph matching to noise and errors at input data and the lack of ge...