Synthesis of Pulsed-Coupled Neural Networks in FPGAs for Real-Time Image Segmentation

This paper describes the implementation of a system based on Pulse Coupled Neural Networks (PCNNs) and Field Programmable Gate Arrays (FPGAs). The PCNN implemented is oriented to the industrial application of segmentation in sequences of images. The work went through several real physical stages of...

Full description

Saved in:
Bibliographic Details
Main Authors: Vega-Pineda, J., Chacon-Murguia, M.I., Camarillo-Cisneros, R.
Format: Conference Proceeding
Language:English
Subjects:
Digital signal processing
Digital systems
Field programmable gate arrays
Image segmentation
Microprocessors
Network synthesis
Neural networks
Neurons
Pixel
Real time systems
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper describes the implementation of a system based on Pulse Coupled Neural Networks (PCNNs) and Field Programmable Gate Arrays (FPGAs). The PCNN implemented is oriented to the industrial application of segmentation in sequences of images. The work went through several real physical stages of implementation and optimization to achieve the needed performance. The greatest performance achieved by the digital system was of 250M pixels per second, enough to process a sequence of images in real time. Details of these stages about the neuron implementation with different Altera's FPGAs families are presented. Furthermore, the implementation is compared with previous implemented schemes based on floating point DSP microprocessor.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2006.246929