A hybrid system for embedded machine vision using FPGAs and neural networks

Authors:
Miguel S. Prieto;Alastair R. Allen
Affiliations:
University of Aberdeen, School of Engineering and Physical Sciences, AB24 3UE, Aberdeen, Scotland, UK;University of Aberdeen, School of Engineering and Physical Sciences, AB24 3UE, Aberdeen, Scotland, UK
Venue:
Machine Vision and Applications
Year:
2009

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Abstract

This paper presents a hybrid model for embedded machine vision combining programmable hardware for the image processing tasks and a digital hardware implementation of an artificial neural network for the pattern recognition and classification tasks. A number of possible architectural implementations are compared. A prototype development system of the hybrid model has been created, and hardware details and software tools are discussed. The applicability of the hybrid design is demonstrated with the development of a vision application: real-time detection and recognition of road signs.