loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 2
Advanced Gaussian MRF Rotation-Invariant Texture Features for Classification of Remote Sensing Imagery
Madison, Wisconsin
June 18-June 20
ISBN: 0-7695-1900-8
ASCII Text
x 
 
Huawu Deng, David A. Clausi, "Advanced Gaussian MRF Rotation-Invariant Texture Features for Classification of Remote Sensing Imagery," Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, vol. 2, pp. 685, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 2, 2003.
 
 
BibTex
x
 
@article{ 10.1109/CVPR.2003.1211533,
author = {Huawu Deng and David A. Clausi},
title = {Advanced Gaussian MRF Rotation-Invariant Texture Features for Classification of Remote Sensing Imagery},
journal ={Computer Vision and Pattern Recognition, IEEE Computer Society Conference on},
volume = {2},
year = {2003},
issn = {1063-6919},
pages = {685},
doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2003.1211533},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
TI - Advanced Gaussian MRF Rotation-Invariant Texture Features for Classification of Remote Sensing Imagery
SN - 1063-6919
SP
EP
A1 - Huawu Deng,
A1 - David A. Clausi,
PY - 2003
KW - null
VL - 2
JA - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
ER -
 
Huawu Deng, University of Waterloo
David A. Clausi, University of Waterloo
The features based on Markov random field (MRF) models are usually sensitive to the rotation of image textures. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for modelling rotated image textures and retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate (LSE) method, an approximate least squares estimate (ALSE) method is proposed to estimate the parameters of the ACGMRF model. The rotation-invariant features can be obtained from the parameters of the ACGMRF model by the one-dimensional (1-D) discrete Fourier transform (DFT). Significantly improved accuracy can be achieved by applying the rotation-invariant features to classify SAR (synthetic aperture radar) sea ice and Brodatz imagery.
Citation:
Huawu Deng, David A. Clausi, "Advanced Gaussian MRF Rotation-Invariant Texture Features for Classification of Remote Sensing Imagery," cvpr, vol. 2, pp.685, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 2, 2003
Usage of this product signifies your acceptance of the Terms of Use.