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Semi-supervised joint learning for hand gesture recognition from a single color image / Chi Xu in Sensors, vol 21 n° 3 (February 2021)  

Public [article]  inSensors > vol 21 n° 3 (February 2021) . - n° 1007 Titre : Semi-supervised joint learning for hand gesture recognition from a single color image Type de document : Article/Communication Auteurs : Chi Xu, Auteur ; Yunkai Jiang, Auteur ; Jun Zhou, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 1007 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] apprentissage semi-dirigé [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] estimation de pose [Termes descripteurs IGN] image en couleur [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] reconnaissance de gestes Résumé : (auteur) Hand gesture recognition and hand pose estimation are two closely correlated tasks. In this paper, we propose a deep-learning based approach which jointly learns an intermediate level shared feature for these two tasks, so that the hand gesture recognition task can be benefited from the hand pose estimation task. In the training process, a semi-supervised training scheme is designed to solve the problem of lacking proper annotation. Our approach detects the foreground hand, recognizes the hand gesture, and estimates the corresponding 3D hand pose simultaneously. To evaluate the hand gesture recognition performance of the state-of-the-arts, we propose a challenging hand gesture recognition dataset collected in unconstrained environments. Experimental results show that, the gesture recognition accuracy of ours is significantly boosted by leveraging the knowledge learned from the hand pose estimation task. Numéro de notice : A2021-160 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/s21031007 date de publication en ligne : 02/02/2021 En ligne : https://doi.org/10.3390/s21031007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97076 [article]

Joint inpainting of depth and reflectance with visibility estimation / Marco Bevilacqua in ISPRS Journal of photogrammetry and remote sensing, vol 125 (March 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 125 (March 2017) . - pp 16 - 32 Titre : Joint inpainting of depth and reflectance with visibility estimation Type de document : Article/Communication Auteurs : Marco Bevilacqua, Auteur ; Jean-François Aujol, Auteur ; Pierre Biasutti , Auteur ; Mathieu Brédif , Auteur ; Aurélie Bugeau, Auteur Année de publication : 2017 Projets : 1-Pas de projet / Article en page(s) : pp 16 - 32 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] carte de profondeur [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] image en couleur [Termes descripteurs IGN] inpainting [Termes descripteurs IGN] point caché [Termes descripteurs IGN] réflectance [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] visibilité Résumé : (Auteur) This paper presents a novel strategy to generate, from 3-D lidar measures, dense depth and reflectance images coherent with given color images. It also estimates for each pixel of the input images a visibility attribute. 3-D lidar measures carry multiple information, e.g. relative distances to the sensor (from which we can compute depths) and reflectances. When projecting a lidar point cloud onto a reference image plane, we generally obtain sparse images, due to undersampling. Moreover, lidar and image sensor positions typically differ during acquisition; therefore points belonging to objects that are hidden from the image view point might appear in the lidar images. The proposed algorithm estimates the complete depth and reflectance images, while concurrently excluding those hidden points. It consists in solving a joint (depth and reflectance) variational image inpainting problem, with an extra variable to concurrently estimate handling the selection of visible points. As regularizers, two coupled total variation terms are included to match, two by two, the depth, reflectance, and color image gradients. We compare our algorithm with other image-guided depth upsampling methods, and show that, when dealing with real data, it produces better inpainted images, by solving the visibility issue. Numéro de notice : A2017-073 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.01.005 date de publication en ligne : 17/01/2017 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2017.01.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84310 [article]

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On the fusion of lidar and aerial color imagery to detect urban vegetation and buildings / Madhurima Bandyopadhyay in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 2 (February 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 2 (February 2017) . - pp 123 - 136 Titre : On the fusion of lidar and aerial color imagery to detect urban vegetation and buildings Type de document : Article/Communication Auteurs : Madhurima Bandyopadhyay, Auteur ; Jan Van Aardt, Auteur ; Kerry Cawse-Nicholson, Auteur ; Emmett Lentilucci, Auteur Année de publication : 2017 Article en page(s) : pp 123 - 136 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image en couleur [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) Three-dimensional (3D) data from light detection and ranging (lidar) sensor have proven advantageous in the remote sensing domain for characterization of object structure and dimensions. Fusion-based approaches of lidar and aerial imagery also becoming popular. In this study, aerial color (RGB) imagery, along with co-registered airborne discrete lidar data were used to separate vegetation and buildings from other urban classes/cover-types, as a precursory step towards the assessment of urban forest biomass. Both spectral and structural features such as object height, distribution of surface normals from the lidar, and a novel vegetation metric derived from combined lidar and RGB imagery, referred to as the lidar-infused vegetation index (LDVI) were used in this classification method. The proposed algorithm was tested on different cityscape regions to verify its robustness. Results showed a good separation of buildings and vegetation from other urban classes with on average an overall classification accuracy of 92 percent, with a kappa statistic of 0.85. These results bode well for the operational fusion of lidar and RGB imagery, often flown on the same platform, towards improved characterization of the urban forest and built environments. Numéro de notice : A2017-039 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.2.123 En ligne : https://doi.org/10.14358/PERS.83.2.123 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84140 [article]

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A manifold alignment approach for hyperspectral image visualization with natural color / Danping Liao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 6 (June 2016) . - pp 3151 - 3162 Titre : A manifold alignment approach for hyperspectral image visualization with natural color Type de document : Article/Communication Auteurs : Danping Liao, Auteur ; Yuntao Qian, Auteur ; Jun Zhou, Auteur ; Yuan Yan Tang, Auteur Année de publication : 2016 Article en page(s) : pp 3151 - 3162 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] alignement multiple semi-dirigé [Termes descripteurs IGN] appariement de points [Termes descripteurs IGN] couleur (variable spectrale) [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image en couleur [Termes descripteurs IGN] image hyperspectrale Résumé : (Auteur) The trichromatic visualization of hundreds of bands in a hyperspectral image (HSI) has been an active research topic. The visualized image shall convey as much information as possible from the original data and facilitate easy image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a new framework for visualizing an HSI with natural color by the fusion of an HSI and a high-resolution color image via manifold alignment. Manifold alignment projects several data sets to a shared embedding space where the matching points between them are pairwise aligned. The embedding space bridges the gap between the high-dimensional spectral space of the HSI and the RGB space of the color image, making it possible to transfer natural color and spatial information in the color image to the HSI. In this way, a visualized image with natural color distribution and fine spatial details can be generated. Another advantage of the proposed method is its flexible data setting for various scenarios. As our approach only needs to search a limited number of matching pixel pairs that present the same object, the HSI and the color image can be captured from the same or semantically similar sites. Moreover, the learned projection function from the hyperspectral data space to the RGB space can be directly applied to other HSIs acquired by the same sensor to achieve a quick overview. Our method is also able to visualize user-specified bands as natural color images, which is very helpful for users to scan bands of interest. Numéro de notice : A2016-849 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2015.2512659 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82930 [article]

A computational introduction to digital image processing / Alasdair McAndrew (2016)

Public Titre : A computational introduction to digital image processing Type de document : Monographie Auteurs : Alasdair McAndrew Mention d'édition : Second edition Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2016 Importance : 535 p. Présentation : illustrations Format : 18 x 26 cm ISBN/ISSN/EAN : 978-1-4822-4732-9 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] compression par ondelettes [Termes descripteurs IGN] GNU Octave [Termes descripteurs IGN] image en couleur [Termes descripteurs IGN] Matlab [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] restauration d'image [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] transformation de Fourier [Termes descripteurs IGN] voisinage (topologie) Index. décimale : 35.20 Traitement d'image Résumé : (Editeur) This book explores the nature and use of digital images and shows how they can be obtained, stored, and displayed. Taking a strictly elementary perspective, the book only covers topics that involve simple mathematics yet offer a very broad and deep introduction to the discipline. This second edition provides users with three different computing options. Along with MATLAB®, this edition now includes GNU Octave and Python. Users can choose the best software to fit their needs or migrate from one system to another. Programs are written as modular as possible, allowing for greater flexibility, code reuse, and conciseness. This edition also contains new images, redrawn diagrams, and new discussions of edge-preserving blurring filters, ISODATA thresholding, Radon transform, corner detection, retinex algorithm, LZW compression, and other topics. Based on the author’s successful image processing courses, this bestseller is suitable for classroom use or self-study. In a straightforward way, the text illustrates how to implement imaging techniques in MATLAB, GNU Octave, and Python. It includes numerous examples and exercises to give students hands-on practice with the material. Note de contenu : 1. Introduction 2. Images Files and File Types 3. Image Display 4. Point Processing 5. Neighborhood Processing 6. Image Geometry 7. The Fourier Transform 8. Image Restoration 9. Image Segmentation 10. Mathematical Morphology 11. Image Topology 12. Shapes and Boundaries 13. Color Processing 14. Image Coding and Compression 15. Wavelets 16. Special Effects Appendix A: Introduction to MATLAB and Octave Appendix B: Introduction to Python Appendix C: The Fast Fourier Transform Numéro de notice : 22951 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91638

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Du photon au pixel / Henri Maître (2015)

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Enabling UAV-based 3D mapping, Pix4D / Anonyme in GIM international, vol 26 n° 7 (July 2012)

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Détection et localisation 3D de panneaux de signalisation [diaporama] / Bahman Soheilian (08/03/2012) 

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Imagerie numérique / Christine Fernandez-Maloigne (2012)

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Extraction de panneaux de signalisation routière dans des images couleurs / Bahman Soheilian (19/01/2010)    

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Colors of the past: color image segmentation in historical topographic maps based on homogeneity / S. Leyk in Geoinformatica, vol 14 n° 1 (January 2010)  

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Traitement de l'image et de la vidéo / Rachid Belaroussi (2010)

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Automatic estimation of fine terrain models from multiple high-resolution satellite images / Nicolas Champion (07/11/2009)

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Efficient shadow detection of color aerial images based on successive thresholding scheme / K.L. Chung in IEEE Transactions on geoscience and remote sensing, vol 47 n° 2 (February 2009)

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Photoshop CS4 pour les nuls / P. Bauer (2009)

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