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Your search keyword '"Département lmage et Traitement Information (IMT Atlantique - ITI)"' showing total 2 results
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2 results on '"Département lmage et Traitement Information (IMT Atlantique - ITI)"'

1. Automatic x-ray image segmentation and clustering for threat detection

Author

Carole Belloni, Nabil Aouf, David Nam, Odysseas Kechagias-Stamatis, Centre for Electronic Warfare (Cranfield University) (CEW), Lab-STICC_IMTA_CID_PRASYS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)

Subjects
020301 aerospace & aeronautics, Computer science, Segmentation-based object categorization, business.industry, TK, Scale-space segmentation, Image processing, 02 engineering and technology, Image segmentation, Domain (software engineering), 0203 mechanical engineering, Region growing, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Segmentation, Computer vision, Artificial intelligence, business, Cluster analysis, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Firearms currently pose a known risk at the borders. The enormous number of X-ray images from parcels, luggage and freight coming into each country via rail, aviation and maritime presents a continual challenge to screening officers. To further improve UK capability and aid officers in their search for firearms we suggest an automated object segmentation and clustering architecture to focus officers' attentions to high-risk threat objects. Our proposal utilizes dual-view single/ dual-energy 2D X-ray imagery and is a blend of radiology, image processing and computer vision concepts. It consists of a triple-layered processing scheme that supports segmenting the luggage contents based on the effective atomic number of each object, which is then followed by a dual-layered clustering procedure. The latter comprises of mild and a hard clustering phase. The former is based on a number of morphological operations obtained from the image-processing domain and aims at disjoining mild-connected objects and to filter noise. The hard clustering phase exploits local feature matching techniques obtained from the computer vision domain, aiming at sub-clustering the clusters obtained from the mild clustering stage. Evaluation on highly challenging single and dual-energy X-ray imagery reveals the architecture's promising performance.

Published
2017

2. SAR image dataset of military ground targets with multiple poses for ATR

Author

Thomas Merlet, Alessio Balleri, Jean-Marc Le Caillec, Nabil Aouf, Carole Belloni, Lab-STICC_IMTA_CID_PRASYS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Centre for Electronic Warfare (Cranfield University) (CEW), Thales optronique lasers (Entreprise), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)

Subjects
Synthetic aperture radar, 02 engineering and technology, 01 natural sciences, Signal, 010309 optics, Set (abstract data type), ISAR, Automatic target recognition, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Range (statistics), Waveform, Computer vision, Remote sensing, business.industry, MGTD, Bandwidth (signal processing), Inverse synthetic aperture radar, Geography, ATR, 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, SAR, Dataset
Abstract

International audience; Automatic Target Recognition (ATR) is the task of automatically detecting and classifying targets. Recognition using Synthetic Aperture Radar (SAR) images is interesting because SAR images can be acquired at night and under any weather conditions, whereas optical sensors operating in the visible band do not have this capability. Existing SAR ATR algorithms have mostly been evaluated using the MSTAR dataset. 1 The problem with the MSTAR is that some of the proposed ATR methods have shown good classification performance even when targets were hidden, 2 suggesting the presence of a bias in the dataset. Evaluations of SAR ATR techniques are currently challenging due to the lack of publicly available data in the SAR domain. In this paper, we presenta high resolution SAR dataset consisting of images of a set of ground military target models taken at various aspect angles, The dataset can be used for a fair evaluation and comparison of SAR ATR algorithms. We applied the Inverse Synthetic Aperture Radar (ISAR) technique to echoes from targets rotating on a turntable and illuminated with a stepped frequency waveform. The targets in the database consist of four variants of two 1.7m-long models of T-64 and T-72 tanks. The gun, the turret position and the depression angle are varied to form 26 different sequences of images. The emitted signal spanned the frequency range from 13 GHz to 18 GHz to achieve a bandwidth of 5 GHz sampled with 4001 frequency points. The resolution obtained with respect to the size of the model targets is comparable to typical values obtained using SAR airborne systems. Single polarized images (Horizontal-Horizontal) are generated using the backprojection algorithm. 3 A total of 1480 images are produced using a 20 ° integration angle. The images in the dataset are organized in a suggested training and testing set to facilitate a standard evaluation of SAR ATR algorithm

Published
2017

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