Paper Info
Title
A Multilayer Markovian Model for Change Detection in Aerial Image Pairs with Large Time Differences
Abstract
In this paper, we propose a Multilayer Markovian model for change detection in registered aerial image pairs with large time differences. A Three Layer Markov Random Field takes into account information from two different sets of features namely the Modified HOG (Histogram of Oriented Gradients) difference and the Gray-Level (GL) Difference. The third layer is the resultant combination of the two layers. Thus we integrate both the texture level as well as the pixel level information to generate the final result. The proposed model uses pair wise interaction retaining the sub-modularity condition for energy. Hence a global energy optimization can be achieved using a standard min-cut/ max flow algorithm ensuring homogeneity in the connected regions.
Year
DOI
Venue
2014
10.1109/ICPR.2014.169
Pattern Recognition
Keywords
Field
DocType
Markov processes,image texture,object detection,Gray-level difference,aerial image pairs,change detection,histogram of oriented gradients,modified HOG,multilayer Markovian model,standard min cut-max flow algorithm,submodularity condition,three layer Markov random field
Computer vision,Histogram,Change detection,Markov process,Pattern recognition,Markov random field,Aerial image,Image segmentation,Histogram of oriented gradients,Artificial intelligence,Pixel,Mathematics
Conference
ISSN
Citations 
PageRank 
1051-4651
2
0.42
References 
Authors
11
3
Name
Order
Citations
PageRank
Praveer Singh1653.05
Zoltan Kato226528.28
Josiane Zerubia32032232.91