loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
33rd Applied Imagery Pattern Recognition Workshop (AIPR'04)
Real Time Texture Classification using Field Programmable Gate Arrays
Cosmos Club, Washington, DC
October 13-October 15
ISBN: 0-7695-2250-5
ASCII Text
x 
 
Geoffrey Wall, Faizal Iqbal, Jason Isaacs, Xiuwen Liu, Simon Foo, "Real Time Texture Classification using Field Programmable Gate Arrays," Applied Image Pattern Recognition Workshop,, pp. 130-135, 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04), 2004.
 
 
BibTex
x
 
@article{ 10.1109/AIPR.2004.38,
author = {Geoffrey Wall and Faizal Iqbal and Jason Isaacs and Xiuwen Liu and Simon Foo},
title = {Real Time Texture Classification using Field Programmable Gate Arrays},
journal ={Applied Image Pattern Recognition Workshop,},
volume = {0},
year = {2004},
issn = {1550-5219},
pages = {130-135},
doi = {http://doi.ieeecomputersociety.org/10.1109/AIPR.2004.38},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Applied Image Pattern Recognition Workshop,
TI - Real Time Texture Classification using Field Programmable Gate Arrays
SN - 1550-5219
SP130
EP135
A1 - Geoffrey Wall,
A1 - Faizal Iqbal,
A1 - Jason Isaacs,
A1 - Xiuwen Liu,
A1 - Simon Foo,
PY - 2004
KW - null
VL - 0
JA - Applied Image Pattern Recognition Workshop,
ER -
 
Geoffrey Wall, Florida State University, Tallahassee, FL; FAMU FSU College of Engineering, Tallahassee, FL
Faizal Iqbal, FAMU FSU College of Engineering, Tallahassee, FL
Jason Isaacs, FAMU FSU College of Engineering, Tallahassee, FL
Xiuwen Liu, Florida State University, Tallahassee, FL
Simon Foo, FAMU FSU College of Engineering, Tallahassee, FL
In this paper we present a novel hardware / software approach to implement a highly accurate texture classification algorithm. We propose the use of field programmable gate arrays (FPGAs) to efficiently compute multiple convolutions in parallel that are required by the spectral histogram representation we employ. The combination of custom hardware and software allows us to have a classifier that is able to achieve results of over 99% accuracy at a rate of roughly 6000 image classifications per second on a challenging real texture dataset.
Citation:
Geoffrey Wall, Faizal Iqbal, Jason Isaacs, Xiuwen Liu, Simon Foo, "Real Time Texture Classification using Field Programmable Gate Arrays," aipr, pp.130-135, 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04), 2004
Usage of this product signifies your acceptance of the Terms of Use.