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.
Original language | English |
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Pages (from-to) | 379-394 |
Number of pages | 16 |
Journal | Machine Vision and Applications |
Volume | 20 |
Issue number | 6 |
Early online date | 27 Mar 2008 |
DOIs | |
Publication status | Published - Oct 2009 |
Keywords
- embedded machine vision
- FPGA
- ANN
- SOM
- road sign detection
- classification
- architecture
- recognition
- hardware