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

Miguel S Prieto, Alastair R Allen

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)379-394
Number of pages16
JournalMachine Vision and Applications
Issue number6
Early online date27 Mar 2008
Publication statusPublished - Oct 2009


  • embedded machine vision
  • FPGA
  • ANN
  • SOM
  • road sign detection
  • classification
  • architecture
  • recognition
  • hardware

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