Une texture polynomiale pour les modèles actifs d'apparence

International audienceDans cet article, nous proposons une nouvelle approche pour la représentation de texture dans les modèles actifs d'apparence (AAM). Celle-ci est basée sur l'utilisation de coefficients issus de projections des intensités lumineuses sur une base polynomiale complète. Parce qu'elle propose une représentation compacte et hiérarchique des images, la décomposition polynomiale est une alternative efficace aux représentations trop globales telles que l'ACP, ou trop redondantes telles que les ondelettes de Gabor. De plus, elle apporte une certaine souplesse par rapport aux représentations en ondelettes dans les paramètres de la décomposition. Nous décrirons comment des coefficients de projection sur bases polynomiales peuvent être utilisés dans un modèle AAM en fournissant des résultats expérimentaux dans un contexte d'alignement de visages. Ceux-ci illustreront la capacité de notre approche à améliorer la robustesse face aux changements de pose et d'expression faciale

A polynomial texture extraction with application in dynamic texture classification

International audienceGeometry and texture image decomposition is an important paradigm in image processing. Following to Yves Meyer works based on Total Variation (VT), the decomposition model has known a renewed interest. In this paper , we propose an algorithm which decomposes color image into geometry and texture component by projecting the image in a bivariate polynomial basis and considering the geometry component as the partial reconstruction and the texture component as the remaining part. The experimental results show the adequacy of using our method as a texture extraction tool. Furthermore, we integrate it into a dynamic texture classification process

Spatial image polynomial decomposition with application to video classification

International audienceThis paper addresses the use of orthogonal polynomial basis transform in video classification due to its multiple advantages, especially for multiscale and multiresolution analysis similar to the wavelet transform. In our approach, we benefit from these advantages to reduce the resolution of the video by using a multiscale/multiresolution decomposition to define a new algorithm that decomposes a color image into geometry and texture component by projecting the image on a bivariate polynomial basis and considering the geometry component as the partial reconstruction and the texture component as the remaining part, and finally to model the features (like motion and texture) extracted from reduced image sequences by projecting them into a bivariate polynomial basis in order to construct a hybrid polynomial motion texture video descriptor. To evaluate our approach, we consider two visual recognition tasks, namely the classification of dynamic textures and recognition of human actions. The experimental section shows that the proposed approach achieves a perfect recognition rate in the Weizmann database and highest accuracy in the Dyntex++ database compared to existing methods

Identification sans contrainte de stationnarité d'un modèle AR d'une texture

Un problème classique en analyse d'image est d'obtenir une modélisation caractéristique d'images texturées. L'utilisation d'un modèle AR-2D fournit souvent des solutions efficaces. Cependant, l'identification des coefficients d'un tel modèle est assujettie à l'hypothèse de stationnarité du signal. Pour résoudre ce problème, nous considérons la distribution spatiale du signal générateur utilisé avec le modèle AR-2D. L'originalité de ce travail vient d'une identification simultanée des paramètres du modèle et des échantillons du générateur, ceci par un processus neuronal. Ainsi, comme résultats nouveaux, nous proposons une méthode permettant de représenter une texture par un couple d'attributs, un modèle AR-2D et une distribution spatiale du signal générateur

Diffusion géométrique pour le masquage d'erreurs de quantification et de transmission sur des images JPEG couleur

Nous proposons ici une méthode de masquage d'erreurs de transmission basée contenue. Contrairement aux schémas classiques de correction d'erreurs de type FEQ/ARQ, notre méthode ne nécessite pas l'ajout de données de contrôle, et exploite directement la redondance spatiale de l'image source. Elle consiste en effet à interpoler les zones valides de l'image reçue dans les zones corrompues, à l'aide d'un processus de diffusion sous contraintes géométriques couplé à une approche multi-résolution. Outre les erreurs de transmission, ce modèle de diffusion nous permet de masquer également les erreurs de quantification (artefacts de compression)

An interferometric study of the Fomalhaut inner debris disk. I. Near-infrared detection of hot dust with VLTI/VINCI

The innermost parts of dusty debris disks around main sequence stars are currently poorly known due to the high contrast and small angular separation with their parent stars. Using near-infrared interferometry, we aim to detect the signature of hot dust around the nearby A4 V star Fomalhaut, which has already been suggested to harbor a warm dust population in addition to a cold dust ring located at about 140 AU. Archival data obtained with the VINCI instrument at the VLTI are used to study the fringe visibility of the Fomalhaut system at projected baseline lengths ranging from 4 m to 140 m in the K band. A significant visibility deficit is observed at short baselines with respect to the expected visibility of the sole stellar photosphere. This is interpreted as the signature of resolved circumstellar emission, producing a relative flux of 0.88% +/- 0.12% with respect to the stellar photosphere. While our interferometric data cannot directly constrain the morphology of the excess emission source, complementary data from the literature allow us to discard an off-axis point-like object as the source of circumstellar emission. We argue that the thermal emission from hot dusty grains located within 6 AU from Fomalhaut is the most plausible explanation for the detected excess. Our study also provides a revised limb-darkened diameter for Fomalhaut (2.223 +/- 0.022 mas), taking into account the effect of the resolved circumstellar emission.Comment: 13 pages, accepted for publication in Ap

Rushes summarization by IRIM consortium: redundancy removal and multi-feature fusion

International audienceIn this paper, we present the first participation of a consortium of French laboratories, IRIM, to the TRECVID 2008 BBC Rushes Summarization task. Our approach resorts to video skimming. We propose two methods to reduce redundancy, as rushes include several takes of scenes. We also take into account low and midlevel semantic features in an ad-hoc fusion method in order to retain only significant content

Time-resolved image analysis for turbulent flows Conference paper

International audienceClassical Particle Image Velocimetry (PIV) uses two representations of the particle image distribution to determine the displacement of the particle image pattern by spatial cross-correlation. The accuracy and the robustness are however limited by the fact that only two representations at t and t +Δt are present. Thus, only a first order approximation of the velocity can be estimated. To enhance the precision in estimating the flow velocity, multi-pulse or multi-frame techniques were already investigated in the early days of PIV as summarized by Adrian (1991) and Hain and Kähler (2007). Today with the increasing power of high repetition rate lasers and enhanced sensitivity of the digital cameras it is possible to have a time-resolved sampling of even aerodynamically relevant flows, were the particles are much smaller than in water flows. The easiest sampling scheme is the equidistant temporal sampling of the particle distribution such that a robust displacement estimation between successive frames (1+2, 2+3, 3+4, ...) is possible. This so called TR-PIV does not only provide the possibility to follow the evolution of flow structures, but offers the ability to strengthen the data processing by using information from more than two frames (e.g. Hain and Kähler, 2007). Within the AFDAR-project (Advanced Flow Diagnostics for Aeronautical Research funded by the European Union) different approaches to evaluat time-resolved image series were developed by the different groups. The current contribution focuses on the comparison of the algorithms that were developed within the AFDAR project by the partners of the consortium. To verify and validate the performance of the different algorithms a short image sequence of an experiment on the flow over periodic hills (ERCOFTAC test case 81) was provided to all partners and evaluated with the current version of the algorithms

IRIM at TRECVID2009: High Level Feature Extraction

International audienceThe IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes our participation to the TRECVID 2009 High Level Features detection task. We evaluated a large number of different descriptors (on TRECVID 2008 data) and tried different fusion strategies, in particular hierarchical fusion and genetic fusion. The best IRIM run has a Mean Inferred Average Precision of 0.1220, which is significantly above TRECVID 2009 HLF detection task median performance. We found that fusion of the classification scores from different classifier types improves the performance and that even with a quite low individual performance, audio descriptors can help

Modeling Debris Disk Evolution

Understanding the formation, evolution, and architectures of planetary systems requires detailed knowledge of their components. Debris disks provide a means with which we can study them. The next decade will deliver a wealth of new information on the nearest systems. Parallel advances in modeling will be necessary to interpret these new datasets