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Publications about mixed Markov models
Result of the query in the list of publications :
Article |
1 - Change Detection in Optical Aerial Images by a Multi-Layer Conditional Mixed Markov Model.
C. Benedek and T. Szirányi. IEEE Trans. Geoscience and Remote Sensing, 47(10): pages 3416-3430, October 2009.
Keywords : mixed Markov models, Change detection, Aerial images, MAP estimation.
Copyright : IEEE
@ARTICLE{benedekTGRS09,
|
| author |
= |
{Benedek, C. and Szirányi, T.}, |
| title |
= |
{Change Detection in Optical Aerial Images by a Multi-Layer Conditional Mixed Markov Model}, |
| year |
= |
{2009}, |
| month |
= |
{October}, |
| journal |
= |
{IEEE Trans. Geoscience and Remote Sensing}, |
| volume |
= |
{47}, |
| number |
= |
{10}, |
| pages |
= |
{3416-3430}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?isnumber=5257398&arnumber=5169964&count=26&index=11}, |
| keyword |
= |
{mixed Markov models, Change detection, Aerial images, MAP estimation} |
| } |
Abstract :
In this paper we propose a probabilistic model for detecting relevant changes in registered aerial image pairs taken with the time differences of several years and in different seasonal conditions. The introduced approach, called the Conditional Mixed Markov model (CXM), is a combination of a mixed Markov model and a conditionally independent random field of signals. The model integrates global intensity statistics with local correlation and contrast features. A global energy optimization process ensures simultaneously optimal local feature selection and smooth, observation-consistent segmentation. Validation is given on real aerial image sets provided by the Hungarian Institute of Geodesy, Cartography and Remote Sensing and Google Earth. |
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2 Conference articles |
1 - Conditional mixed-state model for structural change analysis from very high resolution optical images.
B. Belmudez and V. Prinet and J.F. Yao and P. Bouthemy and X. Descombes. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, July 2009.
Keywords : Change detection, mixed Markov models.
@INPROCEEDINGS{bel09,
|
| author |
= |
{Belmudez, B. and Prinet, V. and Yao, J.F. and Bouthemy, P. and Descombes, X.}, |
| title |
= |
{Conditional mixed-state model for structural change analysis from very high resolution optical images}, |
| year |
= |
{2009}, |
| month |
= |
{July}, |
| booktitle |
= |
{IGARSS}, |
| address |
= |
{Cape Town, South Africa}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00398062/}, |
| keyword |
= |
{Change detection, mixed Markov models} |
| } |
Abstract :
The present work concerns the analysis of dynamic scenes from earth observation images. We are interested in building a map which, on one hand locates places of change, on the other hand, reconstructs a unique visual information of the non-change areas. We show in this paper that such a problem can naturally be takled with conditional mixed-state random field modeling (mixed-state CRF), where the ”mixed state” refers to the symbolic or continous nature of the unknown variable. The maximum a posteriori (MAP) estimation of the CRF is, through the Hammersley-Clifford theorem, turned into an energy minimisation problem. We tested the model on several Quickbird images and illustrate the quality of the results. |
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2 - A Mixed Markov Model for Change Detection in Aerial Photos with Large Time Differences.
C. Benedek and T. Szirányi. In Proc. International Conference on Pattern Recognition (ICPR), Tampa, USA, December 2008.
Keywords : Aerial images, Change detection, mixed Markov models.
@INPROCEEDINGS{benedekICPR08,
|
| author |
= |
{Benedek, C. and Szirányi, T.}, |
| title |
= |
{A Mixed Markov Model for Change Detection in Aerial Photos with Large Time Differences}, |
| year |
= |
{2008}, |
| month |
= |
{December}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Tampa, USA}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/35/91/16/PDF/benedekICPR08.pdf}, |
| keyword |
= |
{Aerial images, Change detection, mixed Markov models} |
| } |
Abstract :
In the paper we propose a novel multi-layer Mixed Markov model for detecting relevant changes in registered aerial images taken with significant time differences. The introduced approach combines global intensity statistics with local correlation and contrast features. A global energy optimization process simultaneously ensures optimal local feature selection and smooth, observation-consistent classification. Validation is given on real aerial photos. |
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