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Postdoc, INRIA Sophia Antipolis
Keywords: Markov Random Fields, Parameter Estimation, Texture, Classification Contact: |
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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 AYIN team, 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:
2018
Book sections
- ref_biblio
- Jon Atli Benediktsson, Gabriele Cavallaro, Falco Nicola, Ihsen Hedhli, Vladimir Krylov, et al.. Remote sensing data fusion: Markov models and mathematical morphology for multisensor, multiresolution, and multiscale image classification. Mathematical Models for Remote Sensing Image Processing: Models and methods for the analysis of 2D satellite and aerial images, Springer, pp.277-323, 2018, ⟨10.1007/978-3-319-66330-2_7⟩. ⟨hal-01632949⟩
- Accès au bibtex
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2014
Journal articles
- ref_biblio
- Aurélie Voisin, Vladimir Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. Supervised Classification of Multi-sensor and Multi-resolution Remote Sensing Images with a Hierarchical Copula-based Approach. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52 (6), pp.3346-3358. ⟨10.1109/TGRS.2013.2272581⟩. ⟨hal-00841234⟩
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2013
Journal articles
- ref_biblio
- Vladimir Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation. IEEE Transactions on Image Processing, 2013, 22 (10), pp.3791-3806. ⟨hal-00820782⟩
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Conference papers
- ref_biblio
- Vladimir Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. False discovery rate approach to image change detection. IEEE International Conf. on Image Processing (ICIP), Sep 2013, Melbourne, Australia. ⟨hal-00841236⟩
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2012
Conference papers
- ref_biblio
- Vladimir Krylov, Gabriele Moser, Aurélie Voisin, Sebastiano B. Serpico, Josiane Zerubia. Change detection with synthetic aperture radar images by Wilcoxon statistic likelihood ratio test. IEEE International Conference on Image Processing 2012, Sep 2012, Orlando, United States. ⟨hal-00724284⟩
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- ref_biblio
- Sebastiano Serpico, Lorenzo Bruzzone, Giovanni Corsini, William J. Emery, Paolo Gamba, et al.. Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures. IEEE IGARSS – International Geoscience and Remote Sensing Symposium, Jul 2012, Munich, Germany. ⟨hal-00729089⟩
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- ref_biblio
- Aurélie Voisin, Vladimir Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. Multiscale classification of very high resolution SAR images of urban areas by Markov random fields, copula functions, and texture extraction. GTTI – Riunione annuale dell’associazione Gruppo nazionale Telecomunicazioni e Tecnologie dell’Informazione, Jun 2012, Cagliari, Italy. ⟨hal-00727404⟩
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Book sections
- ref_biblio
- Vladimir Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. Probability Density Function Estimation for Classification of High Resolution SAR Images. C. Chen. Signal Processing for Remote Sensing, Second Edition, Taylor & Francis, pp.339-363, 2012, 9781439855966. ⟨hal-00729044⟩
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