loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
4th IEEE Southwest Symposium on Image Analysis and Interpretation
A Neural Network Approach to Geographic Image Analysis
Austin, Texas
April 02-April 04
ISBN: 0-7695-0595-3
ASCII Text
x 
 
Plamen Tchimev, Naoya Moritani, Georgi Georgiev, Iren Valova, "A Neural Network Approach to Geographic Image Analysis," Image Analysis and Interpretation, IEEE Southwest Symposium on, pp. 58, 4th IEEE Southwest Symposium on Image Analysis and Interpretation, 2000.
 
 
BibTex
x
 
@article{ 10.1109/IAI.2000.839571,
author = {Plamen Tchimev and Naoya Moritani and Georgi Georgiev and Iren Valova},
title = {A Neural Network Approach to Geographic Image Analysis},
journal ={Image Analysis and Interpretation, IEEE Southwest Symposium on},
volume = {0},
year = {2000},
isbn = {0-7695-0595-3},
pages = {58},
doi = {http://doi.ieeecomputersociety.org/10.1109/IAI.2000.839571},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Image Analysis and Interpretation, IEEE Southwest Symposium on
TI - A Neural Network Approach to Geographic Image Analysis
SN - 0-7695-0595-3
SP
EP
A1 - Plamen Tchimev,
A1 - Naoya Moritani,
A1 - Georgi Georgiev,
A1 - Iren Valova,
PY - 2000
VL - 0
JA - Image Analysis and Interpretation, IEEE Southwest Symposium on
ER -
 
Plamen Tchimev, Tokyo Institute of Technology
Naoya Moritani, Tokyo Institute of Technology
Georgi Georgiev, Eastern Mediterranean University at Gazimagusa
Iren Valova, University of Massachusetts at Dartmouth
We have developed a method based on the precise pixel-to-pixel matching between two images. This is done by automatic generation of displacement vectors, carrying the information of differences between the two images. For generating a layer of vectors defining the information of displacement we are using a neural network with self-learning architecture [1]. The proposed algorithm perform successful mapping, which can be quantitatively measured as 90% correct recognition as demonstrated by the results.
Citation:
Plamen Tchimev, Naoya Moritani, Georgi Georgiev, Iren Valova, "A Neural Network Approach to Geographic Image Analysis," ssiai, pp.58, 4th IEEE Southwest Symposium on Image Analysis and Interpretation, 2000
Usage of this product signifies your acceptance of the Terms of Use.