|
|
Csaba Benedek
Visitor
 Keywords : Texture, Segmentation, Classification, Object extraction
 Contact :
| Mail : | | CsabadotBenedekatinriadotfr | | Phone : | | (33)4-97-15-53-67 | | Fax : | | (33)4-92-38-76-43 | | Postal adress : | | INRIA Sophia Antipolis
2004, route des Lucioles
06902 Sophia Antipolis Cedex
France | | Webpage : | | visit ! |
|
| Abstract :
Aerial image databases have nowadays rich and continuously augmenting content from the last decades. Automatic image indexing through detecting and clustering different changes on photos, taken from the same area, may be crucial for quick and up-to-date content retrieval in several applications. Although most previous inspections dealt with SAR/infrared imagery, the significance of handling purely optical photos is also increasing.
My research goal is to propose advanced stochastic models for detecting changes which are related to human activities (new, altered or disappeared roads, buildings, ploughlands), on airborne photos taken with more months or years time difference. The examined images include both natural and built-in territories. Due to the seasonal changes, altered illumination, results of irrelevant human intervention (such as using crop rotation in the plough lands), the solely available R, G and B sensor information makes this task highly challenging.
I am working with recent techniques of machine learning, stochastic data modeling and image segmentation such as reverse jump estimation, Mixed Markov Models, Conditional or Discriminative Random Fields, and Pairwise Markov Models.
|
Short Bio :
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 |
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. |
Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles.
C. Benedek and X. Descombes and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Istanbul, Turkey, August 2010.
Keywords : Marked point process, 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 |
= |
{August}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Istanbul, Turkey}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00481019/en/}, |
| keyword |
= |
{Marked point process, 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 Remotely Sensed Images with Multiple Birth and Death Dynamics.
C. Benedek and X. Descombes and J. Zerubia. In IEEE Workshop on Applications of Computer Vision (WACV), pages 100-105, Snowbird, Utah, USA, December 2009.
Keywords : Marked point process, Change detection, Aerial images, Building extraction, Satellite images.
@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 |
= |
{December}, |
| 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 |
= |
{Marked point process, Change detection, Aerial images, Building extraction, Satellite images} |
| } |
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.
|
|
All publications in Ariana Research Group
|
|
2011 Activity report
