Publications de Csaba Benedek

Résultat de la recherche dans la liste des publications :

• 3 Articles
• 4 Articles de conférence
• 2 Rapports de recherche et Rapports techniques

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3 Articles

1 - 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.

2 - Detection of Object Motion Regions in Aerial Image Pairs with a Multi-Layer Markovian Model. C. Benedek et T. Szirányi et Z. Kato et J. Zerubia. IEEE Trans. Image Processing, 18(10): pages 2303-2315, octobre 2009. Mots-clés : Change detection, Aerial images, Camera motion, MRF. @ARTICLE{benedekTIP09, author = {Benedek, C. and Szirányi, T. and Kato, Z. and Zerubia, J.}, title = {Detection of Object Motion Regions in Aerial Image Pairs with a Multi-Layer Markovian Model}, year = {2009}, month = {octobre}, journal = {IEEE Trans. Image Processing}, volume = {18}, number = {10}, pages = {2303-2315}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5089480}, keyword = {Change detection, Aerial images, Camera motion, MRF} } Abstract : We propose a new Bayesian method for detecting the regions of object displacements in aerial image pairs. We use a robust but coarse 2-D image registration algorithm. Our main challenge is to eliminate the registration errors from the extracted change map. We introduce a three-layer Markov Random Field model which integrates information from two different features, and ensures connected homogeneous regions in the segmented images. Validation is given on real aerial photos.

3 - Change Detection in Optical Aerial Images by a Multi-Layer Conditional Mixed Markov Model. C. Benedek et T. Szirányi. IEEE Trans. Geoscience and Remote Sensing, 47(10): pages 3416-3430, octobre 2009. Mots-clés : mixed Markov models, Change detection, Aerial images, Estimation MAP. 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 = {octobre}, 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, Estimation MAP} } 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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4 Articles de conférence

1 - 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.

2 - Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics. C. Benedek et X. Descombes et J. Zerubia. Dans IEEE Workshop on Applications of Computer Vision (WACV), pages 100-105, Snowbird, Utah, USA, décembre 2009. Mots-clés : Processus ponctuels marques, Change detection, Aerial images, Building extraction, Imagerie satellitaire. @INPROCEEDINGS{benedekWacv09, author = {Benedek, C. and Descombes, X. and Zerubia, J.}, title = {Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics}, year = {2009}, month = {décembre}, booktitle = {IEEE Workshop on Applications of Computer Vision (WACV)}, pages = {100-105}, address = {Snowbird, Utah, USA}, pdf = {http://hal.archives-ouvertes.fr/docs/00/42/66/18/PDF/benedekWACV09.pdf}, keyword = {Processus ponctuels marques, Change detection, Aerial images, Building extraction, Imagerie satellitaire} } 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. 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.

3 - A Mixed Markov Model for Change Detection in Aerial Photos with Large Time Differences. C. Benedek et T. Szirányi. Dans Proc. International Conference on Pattern Recognition (ICPR), Tampa, USA, décembre 2008. Mots-clés : 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 = {décembre}, 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.

4 - A Multi-Layer MRF Model for Object-Motion Detection in Unregistered Airborne Image-Pairs. C. Benedek et T. Szirányi et Z. Kato et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Vol. 6, pages 141--144, San Antonio, Texas, USA, septembre 2007. Mots-clés : Change detection, Aerial images, Camera motion, MRF. Copyright : Copyright IEEE @INPROCEEDINGS{benedek_ICIP07, author = {Benedek, C. and Szirányi, T. and Kato, Z. and Zerubia, J.}, title = {A Multi-Layer MRF Model for Object-Motion Detection in Unregistered Airborne Image-Pairs}, year = {2007}, month = {septembre}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, volume = {6}, pages = {141--144}, address = {San Antonio, Texas, USA}, url = {http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=4379541&isnumber=4379494&punumber=4378863&k2dockey=4379541@ieeecnfs&query=%28benedek+%3Cin%3E+metadata%29+%3Cand%3E+%284379494+%3Cin%3E+isnumber%29&pos=0}, pdf = {http://web.eee.sztaki.hu/~bcsaba/Publications/Pdf/benedek_icip2007.pdf}, keyword = {Change detection, Aerial images, Camera motion, MRF} } Abstract : In this paper, we give a probabilistic model for automatic change detection on airborne images taken with moving cameras. To ensure robustness, we adopt an unsupervised coarse matching instead of a precise image registration. The challenge of the proposed model is to eliminate the registration errors, noise and the parallax artifacts caused by the static objects having considerable height (buildings, trees, walls etc.) from the difference image. We describe the background membership of a given image point through two different features, and introduce a novel three-layerMarkov Random Field (MRF) model to ensure connected homogenous regions in the segmented image.

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2 Rapports de recherche et Rapports techniques

1 - 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.

2 - A Three-layer MRF model for Object Motion Detection in Airborne Images. C. Benedek et T. Szirányi et Z. Kato et J. Zerubia. Rapport de Recherche 6208, INRIA, juin 2007. Mots-clés : Aerial images, Change detection, Camera motion, MRF. @TECHREPORT{benedek_INRIARR07, author = {Benedek, C. and Szirányi, T. and Kato, Z. and Zerubia, J.}, title = {A Three-layer MRF model for Object Motion Detection in Airborne Images}, year = {2007}, month = {juin}, institution = {INRIA}, type = {Research Report}, number = {6208}, url = {https://hal.inria.fr/inria-00150805}, pdf = {https://hal.inria.fr/inria-00150805}, keyword = {Aerial images, Change detection, Camera motion, MRF} }

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