NEFI: Network Extraction From Images

Networks and network-like structures are amongst the central building blocks of many technological and biological systems. Given a mathematical graph representation of a network, methods from graph theory enable a precise investigation of its properties. Software for the analysis of graphs is widely available and has been applied to graphs describing large scale networks such as social networks, protein-interaction networks, etc. In these applications, graph acquisition, i.e., the extraction of a mathematical graph from a network, is relatively simple. However, for many network-like structures, e.g. leaf venations, slime molds and mud cracks, data collection relies on images where graph extraction requires domain-specific solutions or even manual. Here we introduce Network Extraction From Images, NEFI, a software tool that automatically extracts accurate graphs from images of a wide range of networks originating in various domains. While there is previous work on graph extraction from images, theoretical results are fully accessible only to an expert audience and ready-to-use implementations for non-experts are rarely available or insufficiently documented. NEFI provides a novel platform allowing practitioners from many disciplines to easily extract graph representations from images by supplying flexible tools from image processing, computer vision and graph theory bundled in a convenient package. Thus, NEFI constitutes a scalable alternative to tedious and error-prone manual graph extraction and special purpose tools. We anticipate NEFI to enable the collection of larger datasets by reducing the time spent on graph extraction. The analysis of these new datasets may open up the possibility to gain new insights into the structure and function of various types of networks. NEFI is open source and available http://nefi.mpi-inf.mpg.de

Multiple testing, uncertainty and realistic pictures

We study statistical detection of grayscale objects in noisy images. The object of interest is of unknown shape and has an unknown intensity, that can be varying over the object and can be negative. No boundary shape constraints are imposed on the object, only a weak bulk condition for the object's interior is required. We propose an algorithm that can be used to detect grayscale objects of unknown shapes in the presence of nonparametric noise of unknown level. Our algorithm is based on a nonparametric multiple testing procedure. We establish the limit of applicability of our method via an explicit, closed-form, non-asymptotic and nonparametric consistency bound. This bound is valid for a wide class of nonparametric noise distributions. We achieve this by proving an uncertainty principle for percolation on finite lattices.Comment: This paper initially appeared in January 2011 as EURANDOM Report 2011-004. Link to the abstract at EURANDOM Repository: http://www.eurandom.tue.nl/reports/2011/004-abstract.pdf Link to the paper at EURANDOM Repository: http://www.eurandom.tue.nl/reports/2011/004-report.pd

Robust nonparametric detection of objects in noisy images

We propose a novel statistical hypothesis testing method for detection of objects in noisy images. The method uses results from percolation theory and random graph theory. We present an algorithm that allows to detect objects of unknown shapes in the presence of nonparametric noise of unknown level and of unknown distribution. No boundary shape constraints are imposed on the object, only a weak bulk condition for the object's interior is required. The algorithm has linear complexity and exponential accuracy and is appropriate for real-time systems. In this paper, we develop further the mathematical formalism of our method and explore important connections to the mathematical theory of percolation and statistical physics. We prove results on consistency and algorithmic complexity of our testing procedure. In addition, we address not only an asymptotic behavior of the method, but also a finite sample performance of our test.Comment: This paper initially appeared in 2010 as EURANDOM Report 2010-049. Link to the abstract at EURANDOM repository: http://www.eurandom.tue.nl/reports/2010/049-abstract.pdf Link to the paper at EURANDOM repository: http://www.eurandom.tue.nl/reports/2010/049-report.pd

Remote Sensing for International Stability and Security - Integrating GMOSS Achievements in GMES

The Joint Research Centre of the European Commission hosted a two-day workshop "Remote sensing for international stability and security: integrating GMOSS achievements in GMES". Its aim was to disseminate the scientific and technical achievements of the Global Monitoring for Security and Stability (GMOSS) network of excellence to partners of ongoing and future GMES projects such as RESPOND, LIMES, RISK-EOS,PREVIEW, BOSS4GMES, SAFER, G-MOSAIC. The objectives of this workshop were: ¿ To bring together scientific and technical people from the GMOSS NoE and from thematically related GMES projects. ¿ To discuss and compare alternative technical solutions (e.g. final experimental understanding from GMOSS, operational procedures applied in projects such as RESPOND, pre-operational application procedures foreseen from LIMES, etc.) ¿ To draft a list of technical and scientific challenges relevant in the next future. ¿ To open GMOSS to a wider forum in the JRC This report contains abstracts of the fifteen contributions presented by European researchers. The different presentations addressed pre-processing, feature recognition, change detection and applications which represents also the structure of the report. The second part includes poster abstracts presented during a separate poster session.JRC.G.2-Global security and crisis managemen

Fusion d'images optique et radar à haute résolution pour la mise à jour de bases de données cartographiques

Cette thèse se situe dans le cadre de l'interprétation d'images satellite à haute résolution, et concerne plus spécifiquement la mise à jour de bases de données cartographiques grâce à des images optique et radar à haute résolution. Cette étude présente une chaîne de traitement générique pour la création ou la mise à jour de bases de données représentant les routes ou les bâtiments en milieu urbain. En fonction des données disponibles, différents scénarios sont envisagés. Le traitement est effectué en deux étapes. D'abord nous cherchons les objets qui doivent être retirés de la base de données. La seconde étape consiste à rechercher dans les images de nouveaux objets à ajouter dans la base de données. Pour réaliser ces deux étapes, des descripteurs sont construits dans le but de caractériser les objets d'intérêt dans les images d'entrée. L'inclusion ou élimination des objets dans la base de données est basée sur un score obtenu après fusion des descripteurs dans le cadre de la théorie de Dempster-Shafer. Les résultats présentés dans cette thèse illustrent l'intérêt d'une fusion multi-capteurs. De plus l'intégration aisée de nouveaux descripteurs permet à la chaîne d'être améliorable et adaptable à d'autres objets. ABSTRACT : This work takes place in the framework of high resolution remote sensing image analysis. It focuses on the issue of cartographic database creation or updating with optical and SAR images. The goal of this work is to build a generic processing chain to update or create a cartographic database representing roads and buildings in built-up areas. According to available data, various scenarios are foreseen. The proposed processing chain is composed of two steps. First, if a database is available, the presence of each database object is checked in the images. The second step consist of looking for new objects that should be included in the database. To determine if an object should be present in the updated database, relevant features are extracted from images in the neighborhood of the considered object. Those features are based on caracteristics of roads and buildings in SAR and optical images. The object removal/inclusion in the DB is based on a score obtained by the fusion of features in the framework of the Dempster-Shafer evidence theory. Results highlight the interest of multi sensor fusion. Moreover the chosen framework allows the easy integration of new features in the processing chai