CNRS Image Interaction Laboratory

La Rochelle, France

CNRS Image Interaction Laboratory

La Rochelle, France
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Zorbas D.,CNRS Image Interaction Laboratory | Raveneau P.,CNRS Image Interaction Laboratory | Ghamri-Doudane Y.,CNRS Image Interaction Laboratory
2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings | Year: 2016

Wireless sensor networks (WSNs) consist of nodes with limited power resources. A potential method to prolong the lifespan of a node is the use of an antenna which can harvest energy from radio frequency (RF) signals. In this paper, we model a network consisting of nodes with energy harvesting capabilities and a number of dedicated energy transmitters (ETs) which send data to the nodes. We identify those parameters which affect the consumption of the nodes and we design a method to achieve multi-hop energy transfer between the nodes. However, the ultimate purpose of this paper is to examine whether the cost of the investment of using energy harvesting nodes can be covered by achieving a lower operation cost; that is longer operation times and, thus, less frequent maintenance. We consider three scenarios with different node densities and transmitter populations. Simulation results show that the use of RF-energy harvesting nodes can save a significant amount of energy, while the cost of the investment can be covered in less than 8 years for dense networks. © 2016 IEEE.

Raveneau P.,CNRS Image Interaction Laboratory | Chaput E.,Toulouse 1 University Capitole | Dhaou R.,Toulouse 1 University Capitole | Beylot A.-L.,Toulouse 1 University Capitole
2016 7th International Conference on the Network of the Future, NOF 2016 | Year: 2016

Crowdsensing is, for a few years, a hot topic. Until now, research on crowdsensing mainly focused on scenarios with devices such as smartphones with huge memory and high computive skills. With the development of the Internet of Things (IoT), crowdsensing can be envisaged with other constraints. Indeed, some IoT nodes are mobile but with limitations about storage and processing capabilities, then connectivity disruptions might occur between the nodes. These issues are tackled by a Disruption Tolerant Networking architecture. In this article, we focus on a subset of IoT, Mobile Sensing Networks (MSN). We propose then, a mechanism which respects the constraints of the nodes and maintains high performance. This mechanism, the multi-level FREAK, uses the mean frequency of contacts with the destination. The metrics drives the transmission. Since some nodes might not meet the destination nor nodes meeting the destination, we had the idea of a multi-level metrics to allow these 'disconnected' nodes to transmit data to the destination. We evaluate our proposal through simulations based on several real mobility traces. Our solution outperforms reference replication and quota-based DTN solutions. © 2016 IEEE.

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.

Qazi I.-U.-H.,University of Poitiers | Alata O.,University of Poitiers | Burie J.-C.,CNRS Image Interaction Laboratory | Moussa A.,Abdelmalek Essaadi University | Fernandez-Maloigne C.,University of Poitiers
Pattern Recognition | Year: 2011

This article presents a comparison of different color spaces including RGB, IHLS and La (*)b (*) for color texture characterization. This comparison is based on the fusion of the independent spatial structure and color feature cues. In IHLS and L (*)a (*)b (*), two channel complex color images are created from the luminance and the chrominance values. For such images, two dimensional complex multichannel linear prediction models are used to perform parametric power spectrum estimation and the structure feature cues are computed from this estimated power spectrum. Quantitative comparison of auto spectra of luminance and combined chrominance channels for different color spaces is done. This comparison is based on the degree of decorrelation between luminance and chrominance information provided by different color space transformations. Three dimensional histograms are used as color feature cues. Then, to classify color textures, KullbackLeibler divergence based symmetric distance measures are calculated for pure color, luminance structure and chrominance structure feature cues. Individual as well as combined effect of information from all feature cues on classification results is then compared for different color spaces and different color texture data sets. The proposed color texture classification method performs better than the state of the art methods in certain cases. The L (*)a (*)b (*) color space gives us a better characterization of the chrominance spatial structure as well as the overall spatial structure for all of the chosen data sets. Experimental results on pixel classification of color textures are also presented and discussed. © 2010 Elsevier Ltd. All rights reserved.

Gaugue A.,CNRS Image Interaction Laboratory | Liebe C.,CNRS Image Interaction Laboratory | Combeau P.,French National Center for Scientific Research | Pousset Y.,French National Center for Scientific Research | And 3 more authors.
International Journal of Antennas and Propagation | Year: 2010

This paper presents a new software for design of through-the-wall imaging radars. The first part describes the evolution of a ray tracing simulator, originally designed for propagation of narrowband signals, and then for ultra-wideband signals. This simulator allows to obtain temporal channel response to a wide-band emitter (3GHz to 10GHz). An experimental method is also described to identify the propagation paths. Simulation results are compared to propagation experiments under the same conditions. Different configurations are tested and then discussed. Finally, a configuration of through-the-wall imaging radar is proposed, with different antennas patterns and different targets. Simulated images will be helpful for understanding the experiment obtained images. Copyright © 2010 Christophe Libe et al.

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.

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.

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.,École 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.

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.

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