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Chambéry, France

Brigui F.,Nanyang Technological University | Thirion-Lefevre L.,Supelec | Ginolhac G.,LISTIC | Forster P.,Ecole Normale Superieure de Cachan
IEEE Transactions on Geoscience and Remote Sensing | Year: 2014

We develop a new synthetic aperture radar (SAR) algorithm based on physical models for the detection of a man-made target (MMT) embedded in strong interferences (trunks of a forest). These physical models for the MMT and the interferences are integrated in low-rank subspaces and are based on scattering and polarimetric properties. Several images, called subspace SAR images, can be generated and combined considering these subspace models. We then propose a new approach for target detection and interference reduction based on the combination of SAR subspace images. We show that our SAR algorithm outperforms the classical SAR imagery algorithm on both simulated data and real data in the context of foliage penetration detection. © 2013 IEEE. Source

Brigui F.,Nanyang Technological University | Ginolhac G.,LISTIC | Thirion-Lefevre L.,Supelec | Forster P.,Ecole Normale Superieure de Cachan
IEEE Transactions on Aerospace and Electronic Systems | Year: 2014

We have developed a new synthetic aperture radar (SAR) algorithm based on physical models for the detection of a man-made target (MMT) embedded in strong clutter (trunks in a forest). The physical models for the MMT and the clutter are represented by low-rank subspaces and are based on scattering and polarimetric properties. Our SAR algorithm applies the oblique projection of the received signal along the clutter subspace onto the target subspace. We compute its statistical performance in terms of probabilities of detection and false alarms. The performances of the proposed SAR algorithm are improved compared to those obtained with existing SAR algorithms: the MMT detection is greatly improved, and the clutter is rejected. We also studied the robustness of our SAR algorithm to interference modeling errors. Results on real foliage penetration data showed the usefulness of this approach. © 2014 IEEE. Source

Benoit A.,LISTIC | Caplier A.,CNRS GIPSA Laboratory
Computer Vision and Image Understanding | Year: 2010

This paper proposes to demonstrate the advantages of using certain properties of the human visual system in order to develop a set of fusion algorithms for automatic analysis and interpretation of global and local facial motions. The proposed fusion algorithms rely on information coming from human vision models such as human retina and primary visual cortex previously developed at Gipsa-lab. Starting from a set of low level bio-inspired modules (static and moving contour detector, motion event detector and spectrum analyser) which are very efficient for video data pre-processing, it is shown how to organize them together in order to achieve reliable face motion interpretation. In particular, algorithms for global head motion analysis such as head nods, for local eye motion analysis such as blinking, for local mouth motion analysis such as speech lip motion and yawning and for open/close mouth/eye state detection are proposed and their performances are assessed. Thanks to the use of human vision model pre-processing which decorrelates visual information in a reliable manner, fusion algorithms are simplified and remain robust against traditional video acquisition problems (light changes, object detection failure, etc.). © 2010 Elsevier Inc. Source

Destercke S.,Compiegne University of Technology | Antoine V.,LISTIC
Advances in Intelligent Systems and Computing | Year: 2013

When working with sets of probabilities, basic information fusion operators quickly reach their limits: intersection becomes empty, while union results in a poorly informative model. An attractive means to overcome these limitations is to use maximal coherent subsets (MCS). However, identifying the maximal coherent subsets is generally NP-hard. Previous proposals advocating the use of MCS to merge probability sets have not provided efficient ways to perform this task. In this paper, we propose an efficient approach to do such a merging between imprecise probability masses, a popular model of probability sets, and test it on an ensemble classification problem. © 2013 Springer-Verlag. Source

Combernoux A.,Supelec | Pascal F.,Supelec | Ginolhac G.,LISTIC | Lesturgie M.,Supelec
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2015

The paper addresses the problem of approximating the detector distribution used in target detection embedded in a disturbance composed of a low rank Gaussian noise and a white Gaussian noise. In this context, it is interesting to use an adaptive version of the Low Rank Normalized Matched Filter (LR-ANMF) detector, which is a function of the estimated projector onto the low rank noise subspace. We will show that the traditional approximation of the LR-ANMF detector distribution is not always the better one. In this paper, we propose to perform its limits when the number of secondary data K and the data dimension m both tend to infinity at the same rate m/K → c element 2 (0;∞). Then, we give the theoretical distributions of these limits in the large dimensional regime and approximate the LR-ANMF detector distribution by them. The comparison of empirical and theoretical distributions on a jamming application shows the interest of our approach. © 2015 IEEE. Source

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