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Csaba Benedek
Chercheur invité
 Mots-clés : Texture, Segmentation, Classification, Extraction d'objets
 Contact :
| E-Mail : | | CsabadotBenedekatinriadotfr | | Téléphone : | | (33)4-97-15-53-67 | | Fax : | | (33)4-92-38-76-43 | | Adresse : | | INRIA Sophia Antipolis
2004, route des Lucioles
06902 Sophia Antipolis Cedex
France | | Site personnel : | | visitez ! |
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| Mini CV :
Qualification:
2008 - Ph.D in Image Processing from the Pázmány Péter Catholic University, Faculty of Information Technology, Budapest, Hungary
2004 - M.Sc. in Computer Sciences, Budapest University of Technology and Economics
Research experiences:
2002-2008 emloyed by the Computer and Automation Research Institute (SZTAKI), Hungarian Academy of Scienes |
Dernières publications dans le projet Ariana :
Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics.
C. Benedek et X. Descombes et J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 34(1): pages 33-50, janvier 2012.
Mots-clés : Building extraction, Change detection, Processus ponctuels marques, 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 |
= |
{janvier}, |
| 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, Processus ponctuels marques, 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. |
Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles.
C. Benedek et X. Descombes et J. Zerubia. Dans Proc. International Conference on Pattern Recognition (ICPR), Istanbul, Turkey, août 2010.
Mots-clés : Processus ponctuels marques, multiple birth-and-death dynamics, Building extraction.
@INPROCEEDINGS{benedekICPR10,
|
| author |
= |
{Benedek, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles}, |
| year |
= |
{2010}, |
| month |
= |
{août}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Istanbul, Turkey}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00481019/en/}, |
| keyword |
= |
{Processus ponctuels marques, multiple birth-and-death dynamics, Building extraction} |
| } |
Abstract :
In this paper we introduce a probabilistic approach of building extraction in remotely sensed images. To cope with data heterogeneity we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature based modules. A global optimization process attempts to find the optimal configuration of buildings, considering simultaneously the observed data, prior knowledge, and interactions between the neighboring building parts. The proposed method is evaluated on various aerial image sets containing more than 500 buildings, and the results are matched against two state-of-the-art techniques. |
Building Extraction and Change Detection in Multitemporal Aerial and Satellite Images in a Joint Stochastic Approach.
C. Benedek et X. Descombes et J. Zerubia. Rapport de Recherche 7143, INRIA, Sophia Antipolis, décembre 2009.
Mots-clés : Change detection, Building extraction, Processus ponctuels marques, MAP, multiple birth-and-death dynamics.
@TECHREPORT{benedekRR_09,
|
| author |
= |
{Benedek, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Building Extraction and Change Detection in Multitemporal Aerial and Satellite Images in a Joint Stochastic Approach}, |
| year |
= |
{2009}, |
| month |
= |
{décembre}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7143}, |
| address |
= |
{Sophia Antipolis}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00426615}, |
| keyword |
= |
{Change detection, Building extraction, Processus ponctuels marques, MAP, multiple birth-and-death dynamics} |
| } |
Résumé :
Dans ce rapport, nous proposons une nouvelle méthode probabiliste qui intègre l'extraction de bâtiments et la détection de changements à partir de paires d'images de télédétection. Un algorithme d'optimisation globale permet de trouver la configuration optimale de bâtiments en considérant des observations, des connaissances a priori et des interactions entre des parties voisines de bâtiments. La précision est assurée par une vérification d'un modèle objet bayésien; le coût du calcul est considérablement réduit en utilisant un processus stochastique non-uniforme de naissance d'objets fondé sur des caractéristiques bas-niveaux des images, qui génère des objets pertinents ayant une grande probabilité. |
Abstract :
In this report 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. The accuracy is ensured by a Bayesian object model verification, meanwhile the computational cost is significantly decreased by a non-uniform stochastic object birth process, which proposes relevant objects with higher probability based on low-level image features. |
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Liste complète des publications dans le projet Ariana
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Nouvelle page de démos
