By Topic

A Scalable FPGA Implementation of Cellular Neural Networks for Gabor-type Filtering

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)

We describe an implementation of Gabor-type filters on field programmable gate arrays using the cellular neural network (CNN) architecture. The CNN template depends upon the parameters (e.g., orientation, bandwidth) of the Gabor-type filter and can be modified at runtime so that the functionality of Gabor-type filter can be changed dynamically. Our implementation uses the Euler method to solve the ordinary differential equation describing the CNN. The design is scalable to allow for different pixel array sizes, as well as simultaneous computation of multiple filter outputs tuned to different orientations and bandwidths. For 1024 pixel frames, an implementation on a Xilinx Virtex XC2V1000-4 device uses 1842 slices, operates at 120 MHz and achieves 23,000 Euler iterations over one frame per second.

Published in:

Neural Networks, 2006. IJCNN '06. International Joint Conference on

Date of Conference:

0-0 0