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Xavier Descombes
Researcher, INRIA
 Keywords : Stochastic Geometry, Markov Random Fields, MCMC, Object extraction
 Projects : Mode de Vie (PI), EcoNet (PI), Shapes, Color ODEUR, ARC DADA (PI)
 Demos : see this author's demos
 Contact :
| Mail : | | XavierdotDescombesatinriadotfr | | Phone : | | (33)4-92-38-76-63 | | 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 concerns image analysis using probabilistic models. The first part of my research is devoted to the Markov Random Field approach which includes modeling (the Chien-model,...), estimation (An MCMCML estimator,...) and Optimization (Langevin dynamics,...). The second part concerns the Marked Point Process framework and its application in feature extraction from images (road and hydrographic networks, trees, building,..). |
Last publications in Ariana Research Group :
Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics.
C. Benedek and X. Descombes and J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 34(1): pages 33-50, January 2012.
Keywords : Building extraction, Change detection, Marked point process, multiple birth-and-death dynamics.
Copyright : IEEE
@ARTICLE{benedekPAMI11,
|
| author |
= |
{Benedek, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics}, |
| year |
= |
{2012}, |
| month |
= |
{January}, |
| journal |
= |
{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
| volume |
= |
{34}, |
| number |
= |
{1}, |
| pages |
= |
{33-50}, |
| url |
= |
{http://dx.doi.org/10.1109/TPAMI.2011.94}, |
| keyword |
= |
{Building extraction, Change detection, Marked point process, multiple birth-and-death dynamics} |
| } |
Abstract :
In this paper we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. We present methodological contributions in three key issues: (1) We implement a novel object-change modeling approach based on Multitemporal Marked Point Processes, which simultaneously exploits low level change information between the time layers and object level building description to recognize and separate changed and unaltered buildings. (2) To answering the challenges of data heterogeneity in aerial and satellite image repositories, we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature based modules. (3) To simultaneously ensure the convergence, optimality and computation complexity constraints raised by the increased data quantity, we adopt the quick Multiple Birth and Death optimization technique for change detection purposes, and propose a novel non-uniform stochastic object birth process, which generates relevant objects with higher probability based on low-level image features. |
Tree crown detection in high resolution optical images during the early growth stages of eucalyptus plantations in Brazil.
J. Zhou and C. Proisy and X. Descombes and J. Zerubia and G. Le Maire and Y. Nouvellon and P. Couteron. In Asian Conference on Pattern Recognition (ACPR), Beijing, China, November 2011.
Keywords : tree detection, Eucalyptus plantation, Marked point process, multi-date detection.
@INPROCEEDINGS{Zhou11,
|
| author |
= |
{Zhou, J. and Proisy, C. and Descombes, X. and Zerubia, J. and Le Maire, G. and Nouvellon, Y. and Couteron, P.}, |
| title |
= |
{Tree crown detection in high resolution optical images during the early growth stages of eucalyptus plantations in Brazil}, |
| year |
= |
{2011}, |
| month |
= |
{November}, |
| booktitle |
= |
{Asian Conference on Pattern Recognition (ACPR)}, |
| address |
= |
{Beijing, China}, |
| url |
= |
{http://hal.inria.fr/hal-00740973}, |
| keyword |
= |
{tree detection, Eucalyptus plantation, Marked point process, multi-date detection} |
| } |
Abstract :
Individual tree detection methods are more and more present, and improve, in forestry and silviculture domains with the increasing availability of satellite metric imagery. Automatic detection on these very high spatial resolution images aims to determine the tree positions and crown sizes. In this paper, we used a mathematical model based on marked point processes, which showed advantages w.r.t. several individual tree detection algorithms for plantations, to analyze the eucalyptus plantations in Brazil, with 2 optical images acquired by the WorldView-2 satellite. A tentative detection simultaneously with 2 images of different dates (multi-date) was tested for the first time, which estimates individual tree crown variation during these dates. The relevance of detection was discussed considering the detection performance in tree localizations and crown sizes. Then, tree crown growth was deduced from detection results and compared with the expected dynamics of corresponding populations. |
Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images.
S. Prigent and D. Zugaj and X. Descombes and P. Martel and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011.
@INPROCEEDINGS{prigent11a,
|
| author |
= |
{Prigent, S. and Zugaj, D. and Descombes, X. and Martel, P. and Zerubia, J.}, |
| title |
= |
{Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images}, |
| year |
= |
{2011}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Brussels, Belgium}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/59/06/94/PDF/icip_final.pdf}, |
| keyword |
= |
{} |
| } |
Abstract :
Clinical evaluation of skin treatments consists of two steps. First, the degree of the disease is measured clinically on a group of patients by dermatologists. Then, a statistical test is used on obtained set of measures to determine the treatment efficacy. In this paper, a method is proposed to automatically measure the severity of skin hyperpigmentation. After a classification step, an objective function is designed in order to obtain an optimal linear combination of bands defining the severity criterion. Then a hypothesis test is deployed on this combination to quantify treatment efficacy. |
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All publications in Ariana Research Group
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