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Applying detection proposals to visual tracking for scale and aspect ratio adaptability / Dafei Huang in International journal of computer vision, vol 122 n° 3 (May 2017)
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Titre : Applying detection proposals to visual tracking for scale and aspect ratio adaptability Type de document : Article/Communication Auteurs : Dafei Huang, Auteur ; Lei Luo, Auteur ; Zhaoyun Chen, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 524 – 541 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] détection d'objet
[Termes IGN] filtre adaptatif
[Termes IGN] optimisation (mathématiques)Résumé : (auteur) The newly proposed correlation filter based trackers can achieve appealing performance despite their great simplicity and superior speed. However, this kind of object trackers is not born with scale and aspect ratio adaptability, thus resulting in suboptimal tracking accuracy. To tackle this problem, this paper integrates the class-agnostic detection proposal method, which is widely adopted in object detection area, into a correlation filter tracker. In the tracker part, optimizations such as feature integration, robust model updating and proposal rejection are applied for efficient integration. As for proposal generation, through integrating and comparing four detection proposal generators along with two baseline methods, the quality of detection proposals is found to have considerable influence on tracking accuracy. Therefore, as the most promising proposal generator, EdgeBoxes is chosen and further enhanced with background suppression. Evaluations are mainly performed on a challenging 50-sequence dataset (OTB50) and its two subsets, 28 sequences with significant scale variation and 14 sequences with obvious aspect ratio change. Among the trackers equipped with different proposal generators, state-of-the-art trackers and existing correlation filter variants, our proposed tracker reports the highest accuracy while running efficiently at an average speed of 20.4 frames per second. Additionally, numerical performance analysis in per-sequence manner and experiment results on VOT2014 dataset are also presented to enable deeper insights into our approach. Numéro de notice : A2017-379 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007%2Fs11263-016-0974-6 En ligne : https://doi.org/10.1007/s11263-016-0974-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85930
in International journal of computer vision > vol 122 n° 3 (May 2017) . - pp 524 – 541[article]Geometric calibration of a hyperspectral frame camera / Raquel A. de Oliveira in Photogrammetric record, vol 31 n° 155 (September - November 2016)
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Titre : Geometric calibration of a hyperspectral frame camera Type de document : Article/Communication Auteurs : Raquel A. de Oliveira, Auteur ; Antonio Maria Garcia Tommaselli, Auteur ; Eija Honkavaara, Auteur Année de publication : 2016 Article en page(s) : pp 325 - 347 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] étalonnage géométrique
[Termes IGN] filtre adaptatif
[Termes IGN] image hyperspectrale
[Termes IGN] interféromètreRésumé : (Auteur) Recently, miniaturised hyperspectral sensors operable from small unmanned airborne vehicle platforms have entered the market. The emerging hyperspectral imaging technologies, based on frame cameras and tuneable filters, are attractive alternatives to hyperspectral pushbroom sensors. This paper addresses the geometric calibration process of a hyperspectral frame camera based on a Fabry–Perot interferometer. However, the addition of more optical elements in front of the image sensor can affect the parameters related to the internal geometry of the camera, and a deficiency in knowledge regarding these parameters can have a critical effect on the accuracy of 3D measurements in photogrammetric applications. The experiments focused on assessing the self-calibrating bundle adjustment to verify the behaviour of the interior parameters, considering different spectral bands. The results indicated that the applied self-calibration method can accurately characterise the interior parameters of this camera and that one set of parameters is required for each internal sensor. Numéro de notice : A2016-724 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12153 Date de publication en ligne : 24/08/2016 En ligne : https://doi.org/10.1111/phor.12153 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82256
in Photogrammetric record > vol 31 n° 155 (September - November 2016) . - pp 325 - 347[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2016031 RAB Revue Centre de documentation En réserve L003 Disponible Soil moisture retrieval in agricultural fields using adaptive model-based polarimetric decomposition of SAR data / Lian He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)
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Titre : Soil moisture retrieval in agricultural fields using adaptive model-based polarimetric decomposition of SAR data Type de document : Article/Communication Auteurs : Lian He, Auteur ; Rocco Panciera, Auteur Année de publication : 2016 Article en page(s) : pp 4445 - 4460 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande L
[Termes IGN] biomasse
[Termes IGN] cultures
[Termes IGN] décomposition d'image
[Termes IGN] données polarimétriques
[Termes IGN] filtre adaptatif
[Termes IGN] humidité du sol
[Termes IGN] image radar moirée
[Termes IGN] polarimétrie radarRésumé : (Auteur) The aim of this paper was to estimate soil moisture in agricultural crop fields from fully polarimetric L-band synthetic aperture radar (SAR) data through the polarimetric decomposition of the SAR coherency matrix. A nonnegative-eigenvalue-decomposition scheme, together with an adaptive volume scattering model, is extended to an adaptive model-based decomposition (MBD) (Adaptive MBD) model for soil moisture retrieval. The Adaptive MBD can ensure nonnegative decomposed scattering components and allows two parameters (i.e., the mean orientation angle and a degree of randomness) to be determined to characterize the volume scattering. Its performance was tested using airborne SAR data and coincident ground measurements collected over agricultural fields in southeastern Australia and compared with previous MBD methods (i.e., the Freeman three-component decomposition using the extended Bragg model, the Yamaguchi three-component decomposition, and an iterative generalized hybrid decomposition). The results obtained with the newly proposed decomposition scheme agreed well with expectations based on observed plant structure and biomass levels. The new method was superior in tracking soil moisture dynamics with respect to previous decomposition methods in our study area, with root-mean-square error of soil moisture estimations being 0.10 and 0.14 m3/m3, respectively, for surface and double-bounce components. However, large variability in the achieved soil moisture accuracy was observed, depending on the presence of row structures in the underlying soil surface. Numéro de notice : A2016-884 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2542214 En ligne : https://doi.org/10.1109/TGRS.2016.2542214 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83048
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4445 - 4460[article]Space–time adaptive processing and motion parameter estimation in multistatic passive radar using sparse Bayesian learning / Qisong Wu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)
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Titre : Space–time adaptive processing and motion parameter estimation in multistatic passive radar using sparse Bayesian learning Type de document : Article/Communication Auteurs : Qisong Wu, Auteur ; Yimin D. Zhang, Auteur ; Moeness G. Amin, Auteur ; Brahim Himed, Auteur Année de publication : 2016 Article en page(s) : pp 944 - 957 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage dirigé
[Termes IGN] capteur passif
[Termes IGN] estimation bayesienne
[Termes IGN] estimation des paramètres
[Termes IGN] filtre adaptatif
[Termes IGN] image radar
[Termes IGN] matrice de covarianceMots-clés libres : sparse Bayesian learning Résumé : (Auteur) Conventional space-time adaptive processing suffers from the requirement of a large number of secondary samples. In this paper, a novel method is proposed to accurately estimate the clutter covariance matrix based on a small number of secondary samples, by exploiting the common clutter support across nearby range cells in the angle-Doppler domain. By taking advantage of the intrinsic sparsity of the clutter in the angle-Doppler domain, the recently developed sparse Bayesian learning technique is employed for high-resolution clutter profile estimation. The proposed method does not require the independent and identically distributed secondary sample assumption, and the required number of secondary data samples can be significantly reduced. In addition, we propose a sparse reconstruction-based approach to acquire the 2-D motion parameters of moving targets, by exploiting their group sparsity in the velocity domain in the multistatic passive radar systems. Simulation results verify the effectiveness of the proposed algorithm. Numéro de notice : A2016-118 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2470518 En ligne : https://doi.org/10.1109/TGRS.2015.2470518 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79998
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 944 - 957[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016021 SL Revue Centre de documentation Revues en salle Disponible Spectral–spatial kernel regularized for hyperspectral image denoising full text / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)
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Titre : Spectral–spatial kernel regularized for hyperspectral image denoising full text Type de document : Article/Communication Auteurs : Yuan Yuan, Auteur ; Xianngtao Zheng, Auteur ; Xiaoqiang Lu, Auteur Année de publication : 2015 Article en page(s) : pp 3815 - 3832 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] filtrage du bruit
[Termes IGN] filtre adaptatif
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) Noise contamination is a ubiquitous problem in hyperspectral images (HSIs), which is a challenging and promising theme in many remote sensing applications. A large number of methods have been proposed to remove noise. Unfortunately, most denoising methods fail to take full advantages of the high spectral correlation and to simultaneously consider the specific noise distributions in HSIs. Recently, a spectral-spatial adaptive hyperspectral total variation (SSAHTV) was proposed and obtained promising results. However, the SSAHTV model is insensitive to the image details, which makes the edges blur. To overcome all of these drawbacks, a spectral-spatial kernel method for HSI denoising is proposed in this paper. The proposed method is inspired by the observation that the spectral-spatial information is highly redundant in HSIs, which is sufficient to estimate the clear images. In this paper, a spectral-spatial kernel regularization is proposed to maintain the spectral correlations in spectral dimension and to match the original structure between two spatial dimensions. Moreover, an adaptive mechanism is developed to balance the fidelity term according to different noise distributions in each band. Therefore, it cannot only suppress noise in the high-noise band but also preserve information in the low-noise band. The reliability of the proposed method in removing noise is experimentally proved on both simulated data and real data. Numéro de notice : A2015-318 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2385082 En ligne : https://doi.org/10.1109/TGRS.2014.2385082 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76569
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3815 - 3832[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015071 RAB Revue Centre de documentation En réserve L003 Disponible Color image processing and applications / K.N. Plataniotis (2000)Permalink