Automatic human face tracking in video sequences

La recherche sur la modélisation et la poursuite du visage humain dans des séquences d'images est un des sujets les plus étudiés en Infographie aussi bien qu'en Vision par ordinateur. Les travaux présentés dans cette thèse sont consacrés au problème du suivi 3D du visage dans des séquences vidéos. Ils utilisent une approche basée sur la collaboration entre la vision par ordinateur et l'infographie qui permet de reconstituer la position et l'expression du visage dans chaque image de la séquence. L'approche emploie un modèle spécifique du visage, ainsi qu'un système d'animation permettant de simuler les expressions du visage. Les techniques développées dans cette thèse se décomposent en trois parties. Tout d'abord, nous présentons une approche originale de modélisation 3D d'un visage à partir de seulement quelques images. Elle utilise un mod- èle générique du visage (3D) qui est déformé de manière à ce qu'un certain nombre de points caractéristiques ainsi que les silhouettes de ce modèle 3D se calent sur ceux des images données en entrée. L'accent principal dans l'algorithme de suivi est mis sur la détection précise d'expressions faciales. Dans la deuxième partie nous présentons deux approches de la modélisation d'animation du visage: une approche pseudo-musculaire et une méthode basée sur la spécification du standard MPEG-4. Basée sur ces deux modèles d'animation nous avons développé une technique hybride en les combinant d'une manière hiérarchique. Finalement, dans la troisième et dernière partie nous décrivons notre technique du suivi qui permet d'obtenir la position et l'expression d'un visage sans aucune interaction de la part de l'utilisateur sauf dans la phase d'initialisation du système sur la première imageHuman faces are one of the most researched topics in the domains of both computer graphics and computer vision. This thesis addresses the problem of 3D face tracking from standard video sequences and proposes an approach based upon computer vision/computer graphics collaboration techniques to recover face position and expression in each video frame. Precision of tracking being of major importance, we use a model-based approach with a model adapted to the specific tracked face, and an animation layer built over it. This work consists of three different parts. We start by investigating the possible ways of creation of a customized face model and propose an efficient markerless method for face reconstruction using only a small set of images driving the morphological deformation of a predefined generic mesh. The main focus in tracking is made on precise detection of facial expressions. In this context, several approaches to facial animation are presented: a pseudomuscular approach and a method based on MPEG-4 specification. Based upon these animation systems we developed a hybrid technique by combining them in a hierarchical manner, which allows us to benefit from strong points of both. Finally, in the third and last part we present our tracking technique that allows to obtain the 3D pose and expression of a face in a video sequence without any interaction of the user, except at the initialization stage (first image).PARIS-BIUSJ-Mathématiques rech (751052111) / SudocSudocFranceF

Family of paraxial Laguerre-Gaussian beams with complex shift in Cartesian coordinates

We consider a family of asymmetrical paraxial Laguerre-Gaussian beams with complex shift in Cartesian coordinates. An expression for their orbital angular momentum (OAM) is derived. When the radial index is zero, we determine the coordinates of intensity maximum. We analytically and experimentally show rotation of the crescent-like diffraction pattern during propagation

A global database of soil nematode abundance and functional group composition

As the most abundant animals on earth, nematodes are a dominant component of the soil community. They play critical roles in regulating biogeochemical cycles and vegetation dynamics within and across landscapes and are an indicator of soil biological activity. Here, we present a comprehensive global dataset of soil nematode abundance and functional group composition. This dataset includes 6,825 georeferenced soil samples from all continents and biomes. For geospatial mapping purposes these samples are aggregated into 1,933 unique 1-km pixels, each of which is linked to 73 global environmental covariate data layers. Altogether, this dataset can help to gain insight into the spatial distribution patterns of soil nematode abundance and community composition, and the environmental drivers shaping these patterns.Peer reviewe

Soil nematode abundance and functional group composition at a global scale

Soil organisms are a crucial part of the terrestrial biosphere. Despite their importance for ecosystem functioning, few quantitative, spatially explicit models of the active belowground community currently exist. In particular, nematodes are the most abundant animals on Earth, filling all trophic levels in the soil food web. Here we use 6,759 georeferenced samples to generate a mechanistic understanding of the patterns of the global abundance of nematodes in the soil and the composition of their functional groups. The resulting maps show that 4.4 ± 0.64 × 1020 nematodes (with a total biomass of approximately 0.3 gigatonnes) inhabit surface soils across the world, with higher abundances in sub-Arctic regions (38% of total) than in temperate (24%) or tropical (21%) regions. Regional variations in these global trends also provide insights into local patterns of soil fertility and functioning. These high-resolution models provide the first steps towards representing soil ecological processes in global biogeochemical models and will enable the prediction of elemental cycling under current and future climate scenario

A global database of soil nematode abundance and functional group composition

This study uses direct measurements of soil nematode abundance from 6,825 georeferenced locations around the world, covering all continents and all terrestrial biomes. We describe the data sources, methodology and data processing steps to transform the data into a version that can be used for, for example, geospatial modeling. To do so, the samples were aggregated to the 1-km2 pixel level, each pixel is linked to 73 global covariate layers. These include on soil physiochemical properties, and vegetation, climate, and topographic, anthropogenic, and spectral reflectance information