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Spherically optimized RANSAC aided by an IMU for Fisheye Image Matching / Anbang Liang in Remote sensing, vol 13 n°10 (May 2021)  

Public [article]  inRemote sensing > vol 13 n°10 (May 2021) . - n° 2017 Titre : Spherically optimized RANSAC aided by an IMU for Fisheye Image Matching Type de document : Article/Communication Auteurs : Anbang Liang, Auteur ; Qingquan Li, Auteur ; Zhipeng Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] centrale inertielle [Termes IGN] coordonnées sphériques [Termes IGN] distorsion d'image [Termes IGN] estimation de pose [Termes IGN] étalonnage de chambre métrique [Termes IGN] géométrie épipolaire [Termes IGN] image hémisphérique [Termes IGN] Ransac (algorithme) Résumé : (auteur) Fisheye cameras are widely used in visual localization due to the advantage of the wide field of view. However, the severe distortion in fisheye images lead to feature matching difficulties. This paper proposes an IMU-assisted fisheye image matching method called spherically optimized random sample consensus (So-RANSAC). We converted the putative correspondences into fisheye spherical coordinates and then used an inertial measurement unit (IMU) to provide relative rotation angles to assist fisheye image epipolar constraints and improve the accuracy of pose estimation and mismatch removal. To verify the performance of So-RANSAC, experiments were performed on fisheye images of urban drainage pipes and public data sets. The experimental results showed that So-RANSAC can effectively improve the mismatch removal accuracy, and its performance was superior to the commonly used fisheye image matching methods in various experimental scenarios. Numéro de notice : A2021-416 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13102017 date de publication en ligne : 20/05/2021 En ligne : https://doi.org/10.3390/rs13102017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97757 [article]

A skyline-based approach for mobile augmented reality / Mehdi Ayadi in The Visual Computer, vol 37 n° 4 (April 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 4 (April 2021) . - pp 789 - 804 Titre : A skyline-based approach for mobile augmented reality Type de document : Article/Communication Auteurs : Mehdi Ayadi, Auteur ; Mihaela Scuturici, Auteur ; Chokri Ben Amar, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 789 - 804 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] appariement géométrique [Termes IGN] base de données d'images [Termes IGN] CityGML [Termes IGN] détection de contours [Termes IGN] estimation de pose [Termes IGN] extraction de traits caractéristiques [Termes IGN] Lyon [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] paysage urbain [Termes IGN] réalité augmentée [Termes IGN] superposition d'images [Termes IGN] téléphone intelligent [Termes IGN] vision par ordinateur [Vedettes matières IGN] Géovisualisation Résumé : (auteur) This paper presents a skyline-based approach to enhance the visualization of a new construction project in augmented reality. We propose to process the video stream acquired with a mobile phone to register the real buildings with a 3D city model. We first combine the data acquired with the device’s instruments to estimate a rough user’s pose in the world coordinates system. Then, we use this estimated pose to generate a synthetic image of the user’s view from which we calculate a virtual skyline. In parallel, we extract a real skyline from the real-time video stream. Finally, we match these real and virtual skylines to correct the user’s pose (six degrees of freedom) and thus generate a more realistic augmented reality view. We evaluate the precision and the processing time of our approach using 2D and 3D registration algorithms, as well as with a novel double 2D strategy. Numéro de notice : A2021-342 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-020-01830-8 date de publication en ligne : 06/03/2020 En ligne : https://doi.org/10.1007/s00371-020-01830-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97581 [article]

Visual positioning in indoor environments using RGB-D images and improved vector of local aggregated descriptors / Longyu Zhang in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 4 (April 2021) . - n° 195 Titre : Visual positioning in indoor environments using RGB-D images and improved vector of local aggregated descriptors Type de document : Article/Communication Auteurs : Longyu Zhang, Auteur ; Hao Xia, Auteur ; Qingjun Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 195 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par nuées dynamiques [Termes IGN] estimation de pose [Termes IGN] extraction de traits caractéristiques [Termes IGN] image RVB [Termes IGN] modélisation 3D [Termes IGN] positionnement en intérieur [Termes IGN] Ransac (algorithme) [Termes IGN] scène intérieure [Termes IGN] SIFT (algorithme) [Termes IGN] SURF (algorithme) [Termes IGN] téléphone intelligent [Termes IGN] vision par ordinateur Résumé : (auteur) Positioning information has become one of the most important information for processing and displaying on smart mobile devices. In this paper, we propose a visual positioning method using RGB-D image on smart mobile devices. Firstly, the pose of each image in the training set is calculated through feature extraction and description, image registration, and pose map optimization. Then, in the image retrieval stage, the training set and the query set are clustered to generate the vector of local aggregated descriptors (VLAD) description vector. In order to overcome the problem that the description vector loses the image color information and improve the retrieval accuracy under different lighting conditions, the opponent color information and depth information are added to the description vector for retrieval. Finally, using the point cloud corresponding to the retrieval result image and its pose, the pose of the retrieved image is calculated by perspective-n-point (PnP) method. The results of indoor scene positioning under different illumination conditions show that the proposed method not only improves the positioning accuracy compared with the original VLAD and ORB-SLAM2, but also has high computational efficiency. Numéro de notice : A2021-305 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10040195 date de publication en ligne : 24/03/2021 En ligne : https://doi.org/10.3390/ijgi10040195 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97425 [article]

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 IGN] apprentissage profond [Termes IGN] apprentissage semi-dirigé [Termes IGN] détection d'objet [Termes IGN] estimation de pose [Termes IGN] image en couleur [Termes IGN] jeu de données [Termes 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]

3D hand mesh reconstruction from a monocular RGB image / Hao Peng in The Visual Computer, vol 36 n° 10 - 12 (October 2020)  

Public [article]  inThe Visual Computer > vol 36 n° 10 - 12 (October 2020) . - pp pages2227 - 2239 Titre : 3D hand mesh reconstruction from a monocular RGB image Type de document : Article/Communication Auteurs : Hao Peng, Auteur ; Chuhua Xian, Auteur ; Yunbo Zhang, Auteur Année de publication : 2020 Article en page(s) : pp pages2227 - 2239 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] estimation de pose [Termes IGN] image de synthèse [Termes IGN] image RVB [Termes IGN] maillage [Termes IGN] modélisation 3D [Termes IGN] réalité augmentée [Termes IGN] réalité virtuelle [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] vision monoculaire Résumé : (auteur) Most of the existing methods for 3D hand analysis based on RGB images mainly focus on estimating hand keypoints or poses, which cannot capture geometric details of the 3D hand shape. In this work, we propose a novel method to reconstruct a 3D hand mesh from a single monocular RGB image. Different from current parameter-based or pose-based methods, our proposed method directly estimates the 3D hand mesh based on graph convolution neural network (GCN). Our network consists of two modules: the hand localization and mask generation module, and the 3D hand mesh reconstruction module. The first module, which is a VGG16-based network, is applied to localize the hand region in the input image and generate the binary mask of the hand. The second module takes the high-order features from the first and uses a GCN-based network to estimate the coordinates of each vertex of the hand mesh and reconstruct the 3D hand shape. To achieve better accuracy, a novel loss based on the differential properties of the discrete mesh is proposed. We also use professional software to create a large synthetic dataset that contains both ground truth 3D hand meshes and poses for training. To handle the real-world data, we use the CycleGAN network to transform the data domain of real-world images to that of our synthesis dataset. We demonstrate that our method can produce accurate 3D hand mesh and achieve an efficient performance for real-time applications. Numéro de notice : A2020-596 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01908-3 date de publication en ligne : 14/07/2020 En ligne : https://doi.org/10.1007/s00371-020-01908-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95936 [article]

Guided feature matching for multi-epoch historical image blocks pose estimation / Lulin Zhang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

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Towards structureless bundle adjustment with two- and three-view structure approximation / Ewelina Rupnik in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

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Refractive two-view reconstruction for underwater 3D vision / François Chadebecq in International journal of computer vision, vol 128 n° 5 (May 2020)  

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Automatic scale estimation of structure from motion based 3D models using laser scalers in underwater scenarios / Klemen Istenič in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

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Intelligent Imaging and Analysis / DaeEun Kim (2020)  

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Robust pose estimation and calibration of catadioptric cameras with spherical mirrors / Sagi Filin in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 1 (January 2020)  

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Simulation and analysis of photogrammetric UAV image blocks: influence of camera calibration error / Yilin Zhou in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W5 (May 2019)  

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BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images / Debaditya Acharya in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

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BIM-Tracker: A model-based visual tracking approach for indoor localisation using a 3D building model / Debaditya Acharya in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

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Equivalent constraints for two-view geometry : Pose solution/pure rotation identification and 3D reconstruction / Qi Cai in International journal of computer vision, vol 127 n° 2 (February 2019)  

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