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Time filter

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La Rochelle, France

Soussi M.A.A.B.,Ecole Superieure des Geometres et Topographes ESGT L2G IRSTV | Follin J.-M.,Ecole Superieure des Geometres et Topographes ESGT L2G IRSTV | Moreau G.,Ecole Centrale Nantes | Bouju A.,CNRS Image Interaction Laboratory | Polidori L.,Ecole Superieure des Geometres et Topographes ESGT L2G IRSTV
Ingenierie des Systemes d'Information | Year: 2012

Nowadays, several studies are concerned with the utilization of data resulting from mobile objects along their trips. We formulate the assumption that the database of trajectories of French emergency units (SMUR) can provide useful information for their research of the best intervention routes. In this article we propose an observation based methodology in order to enrich a road network database. These data will be useful for computing the best route in an emergency context. To facilitate the use of our method we propose an approach which is primarily based on the ATD (abstract data types) and secondly we show how to add a conceptual model for these types. We also show our approach to estimate the travel time thanks to two methods: direct measurement and spatio-temporal extrapolation in the absence of observations. © 2012 Lavoisier. Source


Dubois S.,French National Center for Scientific Research | Peteri R.,Laboratoire Mathematiques Image et Applications | Menard M.,CNRS Image Interaction Laboratory
Signal, Image and Video Processing | Year: 2015

The research context of this article is the recognition and description of dynamic textures. In image processing, the wavelet transform has been successfully used for characterizing static textures. To our best knowledge, only two works are using spatio-temporal multiscale decomposition based on the tensor product for dynamic texture recognition. One contribution of this article is to analyze and compare the ability of the 2D+T curvelet transform, a geometric multiscale decomposition, for characterizing dynamic textures in image sequences. Two approaches using the 2D+T curvelet transform are presented and compared using three new large databases. A second contribution is the construction of these three publicly available benchmarks of increasing complexity. Existing benchmarks are either too small not available or not always constructed using a reference database. Feature vectors used for recognition are described as well as their relevance, and performances of the different methods are discussed. Finally, future prospects are exposed. © 2013, Springer-Verlag London. Source


Qazi I.-U.-H.,University of Poitiers | Moussa A.,Abdelmalek Essaadi University | Alata O.,University of Poitiers | Burie J.C.,CNRS Image Interaction Laboratory | Fernandez-Maloigne C.,University of Poitiers
Proceedings - 5th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2009 | Year: 2010

This paper presents a comparison of parametric and non-parametric models of multichannel linear prediction error for supervised color texture segmentation. Information of both luminance and chrominance spatial variation feature cues are used to characterize color textures. The method presented consists of two steps. In the first step, we estimate the linear prediction errors of color textures computed on small training sub images. Multichannel complex versions of linear prediction models are used as image observation models in RGB, IHLS and L*a*b* color spaces. In the second step, overall color distribution of the image is estimated from the multichannel prediction error sequences. Both parametric and non-parametric approaches are used for this purpose. A multivariate Gaussian probability approximation is used as the parametric law defining this color distribution. For non-parametric approximation, we have used a multivariate version of k-nearest neighbor algorithm. Error rate, based on well classified pixels, for different linear prediction models using different color spaces are compared and discussed. © 2009 IEEE. Source


Malki J.,CNRS Image Interaction Laboratory | Wannous R.,CNRS Image Interaction Laboratory | Bouju A.,CNRS Image Interaction Laboratory | Vincent C.,CNRS Coastal and Marine Environment Laboratory
Control and Cybernetics | Year: 2012

Nowadays, with growing use of location-aware, wire-lessly connected, mobile devices, we can easily capture trajectories of mobile objects. To exploit these raw trajectories, we need to enhance them with semantic information. Several research fields are currently focusing on semantic trajectories to support inferences and queries to help users validate and discover more knowledge about mobile objects. The inference mechanism is needed for queries on semantic trajectories connected to other sources of information. Time and space knowledge are fundamental sources of information used by the inference operation on semantic trajectories. This article discusses new approach for inference mechanisms on semantic trajectories. The proposed solution is based on an ontological approach for modelling semantic trajectories integrating time concepts and rules. We present a case study with experiments, optimization and evaluation to show the complexity of inference and queries. Then, we introduce a refinement algorithm based on temporal neighbour to enhance temporal inference. The results show the positive impact of our proposal on reducing the complexity of the inference mechanism. Source


Franco P.,CNRS Image Interaction Laboratory | Ogier J.-M.,CNRS Image Interaction Laboratory | Loonis P.,CNRS Laboratory of Electronics Informatics and Images | Mullot R.,CNRS Image Interaction Laboratory
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

Recently we have developed a model for shape description and matching. Based on minimum spanning trees construction and specifics stages like the mixture, it seems to have many desirable properties. Recognition invariance in front shift, rotated and noisy shape was checked through median scale tests related to GREC symbol reference database. Even if extracting the topology of a shape by mapping the shortest path connecting all the pixels seems to be powerful, the construction of graph induces an expensive algorithmic cost. In this article we discuss on the ways to reduce time computing. An alternative solution based on image compression concepts is provided and evaluated. The model no longer operates in the image space but in a compact space, namely the Discrete Cosine space. The use of block discrete cosine transform is discussed and justified. The experimental results led on the GREC2003 database show that the proposed method is characterized by a good discrimination power, a real robustness to noise with an acceptable time computing. © 2010 Springer-Verlag. Source

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