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Vladimir Krylov
Postdoc, INRIA Sophia Antipolis
 Keywords : Markov Random Fields, Parameter Estimation, Texture, Classification
 Demo : see this author's demo
 Contact :
| Mail : | | vladimirdotkrylovatinriadotfr | | Phone : | | (33)4-89-73-24-43 | | Fax : | | (33)4-92-38-76-43 | | Postal adress : | | INRIA Sophia Antipolis
2004, route des Lucioles
06902 Sophia Antipolis Cedex
France | | Webpage : | | visit ! |
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| Abstract :
| My work at INRIA concerns the statistical modelling of high resolution SAR data. The first part concerns the probability density function estimation via finite mixture approach following the dictionary-based stochastic expectation maximization technique. The second part concerns the application of pdf modeling to high and very high resolution SAR classification by combining the above mentioned finite mixture approach with Markov random field model to bayesian classification. |
Short Bio :
Vladimir A. Krylov received the “specialist” (M.S.) degree in applied mathematics and the “candidate of physico-mathematical sciences” (Ph.D.) degree in mathematics (with specialization in probability theory and mathematical statistics) both from the Lomonosov Moscow State University, Moscow, Russia, in 2007 and 2011, respectively.
Since 2008 he has been collaborating as a visiting Ph.D. student with Ariana research group, INRIA Sophia Antipolis, France. Currently he is working as a Postdoctoral Fellow with ARIANA project, INRIA. His main research interest lies in the field of statistical image analysis and processing. In particular, his work concerns statistical modeling, classification and multitemporal change detection of remote sensing imagery. |
Last publications in Ariana Research Group :
Change detection with synthetic aperture radar images by Wilcoxon statistic likelihood ratio test.
V. Krylov and G. Moser and A. Voisin and S.B. Serpico and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Orlando, United States, October 2012.
@INPROCEEDINGS{ICIP12,
|
| author |
= |
{Krylov, V. and Moser, G. and Voisin, A. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Change detection with synthetic aperture radar images by Wilcoxon statistic likelihood ratio test}, |
| year |
= |
{2012}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Orlando, United States}, |
| url |
= |
{http://hal.inria.fr/hal-00724284}, |
| keyword |
= |
{} |
| } |
Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model.
A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In EURASIP, Bucarest, Romania, August 2012.
@INPROCEEDINGS{EURASIP12,
|
| author |
= |
{Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model}, |
| year |
= |
{2012}, |
| month |
= |
{August}, |
| booktitle |
= |
{EURASIP}, |
| address |
= |
{Bucarest, Romania}, |
| url |
= |
{http://hal.inria.fr/hal-00723286}, |
| keyword |
= |
{} |
| } |
Probability Density Function Estimation for Classification of High Resolution SAR Images.
V. Krylov and G. Moser and S. Serduc and J. Zerubia. In Signal Processing for Remote Sensing, Second Edition, pages 339-363, Ed. C. Chen., Publ. Taylor & Francis, February 2012.
@INCOLLECTION{Taylor12,
|
| author |
= |
{Krylov, V. and Moser, G. and Serduc, S. and Zerubia, J.}, |
| title |
= |
{Probability Density Function Estimation for Classification of High Resolution SAR Images}, |
| year |
= |
{2012}, |
| month |
= |
{February}, |
| booktitle |
= |
{Signal Processing for Remote Sensing, Second Edition}, |
| pages |
= |
{339-363}, |
| editor |
= |
{C. Chen.}, |
| publisher |
= |
{Taylor & Francis}, |
| url |
= |
{https://www.crcpress.com/Signal-and-Image-Processing-for-Remote-Sensing-Second-Edition/Chen/9781439855966}, |
| pdf |
= |
{https://hal.inria.fr/hal-00729044/document}, |
| keyword |
= |
{} |
| } |
|
All publications in Ariana Research Group
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