Updating a reference image for detecting motion in urban scenes

We present in this paper a construction and updating method of a reference image for motion detection in an urban environment . The proposed detection algorithm exploits differences between static edges of the scene and edges extracted from each imag e of the sequence . It allows to detect moving objects contours and moving areas contours if the background is not uniform . Th e reference image, robustly controlled, contains spatio temporal information of a great number of successive images . Updated locally with a recursive filter, it allows to integrate, after a controlled time, objects that stop in the scene . This kind of updating permits to automatically adapt with unpredictible movements of deformable or rigid objects (changes in speed and direction) . I n addition, analyzing edges allows to take into account global illumination changes and heterogeneity of the scene background i n an urban environment . This approach gives good results on complex outdoor image sequences .Nous présentons dans cet article une méthode de construction et de mise à jour d'une image de référence pour la détection du mouvement dans une scène urbaine. L'algorithme de détection proposé exploite les différences entre les contours statiques contenus dans la scène et les contours extraits de chaque image de la séquence. Il permet de mettre en évidence les contours des objets mobiles et les contours des zones affectées par le mouvement dans le cas où le fond n'est pas uniforme. L'image de référence, contrôlée de manière robuste, englobe les informations spatiales et temporelles contenues dans un grand nombre d'images successives de la séquence. Actualisée localement par l'intermédiaire d'un filtre récursif, elle permet d'intégrer, après un temps contrôlé, les objets qui s'arrêtent dans la scène. Ce type de réactualisation permet de s'adapter aux mouvements imprévisibles des objets (changement de vitesse et de direction) déformables ou non. De plus, l'analyse des contours a permis de s'affranchir des variations globales de l'éclairage ainsi que de l'hétérogénéité des fonds de la scène en milieu urbain. Cette approche obtient des résultats satisfaisants sur des images de scènes d'extérieur complexes

Evidential Bagging: Combining Heterogeneous Classifiers in the Belief Functions Framework

International audienceIn machine learning, Ensemble Learning methodologies are known to improve predictive accuracy and robustness. They consist in the learning of many classifiers that produce outputs which are finally combined according to different techniques. Bagging, or Bootstrap Aggre-gating, is one of the most famous Ensemble methodologies and is usually applied to the same classification base algorithm, i.e. the same type of classifier is learnt multiple times on bootstrapped versions of the initial learning dataset. In this paper, we propose a bagging methodology that involves different types of classifier. Classifiers' probabilist outputs are used to build mass functions which are further combined within the belief functions framework. Three different ways of building mass functions are proposed; preliminary experiments on benchmark datasets showing the relevancy of the approach are presented

Exporting and labor demand : micro-level evidence from Germany

It is widely believed that globalization affcts the extent of employment and wage responses to economic shocks. To provide evidence for this, we analyze the effect of firms' exporting behavior on the elasticity of labor demand. Using rich, German administrative linked employer-employee panel data from 1996 to 2008, we explicitly control for self-selection into exporting and endogeneity concerns. In line with our theoretical model, we find that exporting at both the intensive and extensive margins significantly increases the (absolute value of the) unconditional own-wage labor demand elasticity. This is not only true for the average worker, but also for different skill groups. For the median firm, the elasticity is three-quarters higher when comparing exporting to nonexporting firms