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3D map creation using crowdsourced GNSS data / Terence Lines in Computers, Environment and Urban Systems, vol 89 (September 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 89 (September 2021) Titre : 3D map creation using crowdsourced GNSS data Type de document : Article/Communication Auteurs : Terence Lines, Auteur ; Anahid Basiri, Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] approche participative [Termes IGN] Bootstrap (statistique) [Termes IGN] cartographie 3D [Termes IGN] données GNSS [Termes IGN] données localisées 2,5D [Termes IGN] hauteur du bâti [Termes IGN] interface de programmation [Termes IGN] régression logistique [Termes IGN] signal GNSS [Termes IGN] trajet multiple [Termes IGN] vision par ordinateur Résumé : (auteur) 3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. The paper applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard. Numéro de notice : A2021-535 Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101671 date de publication en ligne : 19/06/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101671 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97998 [article]

A shape transformation-based dataset augmentation framework for pedestrian detection / Zhe Chen in International journal of computer vision, vol 129 n° 4 (April 2021)  

Public [article]  inInternational journal of computer vision > vol 129 n° 4 (April 2021) . - pp 1121 - 1138 Titre : A shape transformation-based dataset augmentation framework for pedestrian detection Type de document : Article/Communication Auteurs : Zhe Chen, Auteur ; Wanli Ouyang, Auteur ; Tongliang Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1121 - 1138 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage par transformation [Termes IGN] apprentissage profond [Termes IGN] déformation d'objet [Termes IGN] détection de piéton [Termes IGN] jeu de données [Termes IGN] synthèse d'image [Termes IGN] vision par ordinateur Résumé : (auteur) Deep learning-based computer vision is usually data-hungry. Many researchers attempt to augment datasets with synthesized data to improve model robustness. However, the augmentation of popular pedestrian datasets, such as Caltech and Citypersons, can be extremely challenging because real pedestrians are commonly in low quality. Due to the factors like occlusions, blurs, and low-resolution, it is significantly difficult for existing augmentation approaches, which generally synthesize data using 3D engines or generative adversarial networks (GANs), to generate realistic-looking pedestrians. Alternatively, to access much more natural-looking pedestrians, we propose to augment pedestrian detection datasets by transforming real pedestrians from the same dataset into different shapes. Accordingly, we propose the Shape Transformation-based Dataset Augmentation (STDA) framework. The proposed framework is composed of two subsequent modules, i.e. the shape-guided deformation and the environment adaptation. In the first module, we introduce a shape-guided warping field to help deform the shape of a real pedestrian into a different shape. Then, in the second stage, we propose an environment-aware blending map to better adapt the deformed pedestrians into surrounding environments, obtaining more realistic-llooking pedestrians and more beneficial augmentation results for pedestrian detection. Extensive empirical studies on different pedestrian detection benchmarks show that the proposed STDA framework consistently produces much better augmentation results than other pedestrian synthesis approaches using low-quality pedestrians. By augmenting the original datasets, our proposed framework also improves the baseline pedestrian detector by up to 38% on the evaluated benchmarks, achieving state-of-the-art performance. Numéro de notice : A2021-354 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s11263-020-01412-0 date de publication en ligne : 09/01/2021 En ligne : https://doi.org/10.1007/s11263-020-01412-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97606 [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 : A2021-481 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]

Lightweight convolutional neural network-based pedestrian detection and re-identification in multiple scenarios / Xiao Ke in Machine Vision and Applications, vol 32 n° 2 (March 2021)  

Public [article]  inMachine Vision and Applications > vol 32 n° 2 (March 2021) . - n° 46 Titre : Lightweight convolutional neural network-based pedestrian detection and re-identification in multiple scenarios Type de document : Article/Communication Auteurs : Xiao Ke, Auteur ; Xinru Lin, Auteur ; Liyun Qin, Auteur Année de publication : 2021 Article en page(s) : n° 46 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] cible mobile [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] comportement [Termes IGN] détection de piéton [Termes IGN] reconnaissance de formes [Termes IGN] vision par ordinateur Résumé : (auteur) Pedestrian detection and re-identification technology is a research hotspot in the field of computer vision. This technology currently has issues such as insufficient pedestrian expression ability, occlusion, diverse pedestrian attitude, and difficulty of small-scale pedestrian detection. In this paper, we proposed an end-to-end pedestrian detection and re-identification model in real scenes, which can effectively solve these problems. In our model, the original images are processed with a non-overlapped image blocking data augmentation method, and then input them into the YOLOv3 detector to obtain the object position information. LCNN-based pedestrian re-identification model is used to extract the features of the object. Furthermore, the eigenvectors of the object and the detected pedestrians are calculated, and the similarity between them are used to determine whether they can be marked as target pedestrians. Our method is lightweight and end-to-end, which can be applied to the real scenes. Numéro de notice : A2021-455 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-021-01169-7 date de publication en ligne : 24/02/2021 En ligne : https://doi.org/10.1007/s00138-021-01169-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97900 [article]

Unsupervised deep representation learning for real-time tracking / Ning Wang in International journal of computer vision, vol 129 n° 2 (February 2021)  

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Comparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)  

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A novel deep learning instance segmentation model for automated marine oil spill detection / Shamsudeen Temitope Yekeen in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

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Water level prediction from social media images with a multi-task ranking approach / P. Chaudhary in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

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Indoor positioning using PnP problem on mobile phone images / Hana Kubickova in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

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Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks / Mahmoud Saeedimoghaddam in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

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Comparing the roles of landmark visual salience and semantic salience in visual guidance during indoor wayfinding / Weihua Dong in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)  

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Adaptive Statistical Superpixel Merging With Edge Penalty for PolSAR Image Segmentation / Deliang Xiang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

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Street-Frontage-Net: urban image classification using deep convolutional neural networks / Stephen Law in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

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Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data / Ehsan Farahbakhsh in International Journal of Remote Sensing IJRS, vol 41 n°5 (01 - 08 février 2020)  

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