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Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery / Yiping Gong in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 1 (January 2020) . - pp 34 - 44 Titre : Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery Type de document : Article/Communication Auteurs : Yiping Gong, Auteur ; Zhifeng Xiao, Auteur ; Xiaowei Tan, Auteur Année de publication : 2020 Article en page(s) : pp 34 - 44 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse contextuelle [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] description multiniveau [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] vision par ordinateur [Termes descripteurs IGN] zone d'intérêt Résumé : (auteur) Object detection in very-high-resolution (VHR) remote sensing imagery remains a challenge. Environmental factors, such as illumination intensity and weather, reduce image quality, resulting in poor feature representation and limited detection accuracy. To enrich the feature representation and mine the underlying context information among objects, this article proposes a context-aware convolutional neural network (CA-CNN) model for object detection that includes proposal generation, context feature extraction, feature fusion, and classification. During feature extraction, we propose integrating a context-regions-of-interests (Context-RoIs) mining layer into the CNN model and extracting context features by mapping Context-RoIs mined from the foreground proposals to multilevel feature maps. Finally, the context features extracted from multilevel layers are fused into a single layer, and the proposals represented by the fused features are classified by a softmax classifier. In this article, through numerous experiments, we thoroughly explore the influence of key factors, such as Context-RoIs, different feature scales, and different spatial context window sizes. Because of the end-to-end network design approach, our proposed model simultaneously maintains high efficiency and effectiveness. We conducted all model testing on the public NWPU VHR-10 data set. The experimental results demonstrate that our proposed CA-CNN model achieves significantly improved model performance and better detection results compared with the state-of-the-art methods. Numéro de notice : A2020-038 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2930246 date de publication en ligne : 23/09/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2930246 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94492 [article]

Innovative techniques of photogrammetry for 3D modeling / Vicenzo Barrile in Applied geomatics, Vol 11 n° 4 (December 2019)  

Public [article]  inApplied geomatics > Vol 11 n° 4 (December 2019) . - pp 353–369 Titre : Innovative techniques of photogrammetry for 3D modeling Type de document : Article/Communication Auteurs : Vicenzo Barrile, Auteur ; Alice Pozzoli, Auteur ; Giuliana Bilotta, Auteur ; Antonino Fotia, Auteur Année de publication : 2019 Article en page(s) : pp 353–369 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie analytique [Termes descripteurs IGN] Italie [Termes descripteurs IGN] modèle non linéaire [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] orientation absolue [Termes descripteurs IGN] orientation automatique [Termes descripteurs IGN] orientation relative [Termes descripteurs IGN] Raspberry Pi [Termes descripteurs IGN] reconstruction d'image [Termes descripteurs IGN] structure-from-motion [Termes descripteurs IGN] vision par ordinateur Résumé : (auteur) This note presents the experimental results deriving from the application of two innovative photogrammetric techniques (with particular reference to non-conventional photogrammetric applications) for the production of time-space 3D models of the marine surface. Moreover, the first method (automatic three images processing (ATIP)) proposes some easy procedures to solve typical non-linear problems of analytical photogrammetry. In particular, once validated the technique of orientation of two images (two-step procedure based on two phases: relative orientation and absolute orientation, both characterized by non-linear functions), we propose a procedure for the automatic orientation of three images (the introduction of a third image allows avoiding human decision to find the final solution). The second method (Computer Vision Raspberry Pi—CVR) refers to the use of the “prompt” technique of computer vision (structure from motion) using five appropriately synchronized cameras to acquire simultaneously the various frames, thanks to the use of an acquisition system based on the use of Raspberry Pi. The experimentation was conducted both in the laboratory (on a model that allows to study a typical phenomenon of the Alpine Valtellina region, in the North of Italy) that directly at sea (on a portion of marine surface located in Reggio Calabria near the seafront). The results obtained show a substantial comparability of the results both between the two methods and with the actual data measured at sea with dedicated instrumentation. Numéro de notice : A2019-533 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00264-9 date de publication en ligne : 22/05/2019 En ligne : https://doi.org/10.1007/s12518-019-00264-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94126 [article]

Context pyramidal network for stereo matching regularized by disparity gradients / Junhua Kang in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 201 - 215 Titre : Context pyramidal network for stereo matching regularized by disparity gradients Type de document : Article/Communication Auteurs : Junhua Kang, Auteur ; Lin Chen, Auteur ; Fei Deng, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 201 - 215 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] appariement de formes [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] gradient [Termes descripteurs IGN] vision par ordinateur [Termes descripteurs IGN] vision stéréoscopique Résumé : (Auteur) Also after many years of research, stereo matching remains to be a challenging task in photogrammetry and computer vision. Recent work has achieved great progress by formulating dense stereo matching as a pixel-wise learning task to be resolved with a deep convolutional neural network (CNN). However, most estimation methods, including traditional and deep learning approaches, still have difficulty to handle real-world challenging scenarios, especially those including large depth discontinuity and low texture areas. To tackle these problems, we investigate a recently proposed end-to-end disparity learning network, DispNet (Mayer et al., 2015), and improve it to yield better results in these problematic areas. The improvements consist of three major contributions. First, we use dilated convolutions to develop a context pyramidal feature extraction module. A dilated convolution expands the receptive field of view when extracting features, and aggregates more contextual information, which allows our network to be more robust in weakly textured areas. Second, we construct the matching cost volume with patch-based correlation to handle larger disparities. We also modify the basic encoder-decoder module to regress detailed disparity images with full resolution. Third, instead of using post-processing steps to impose smoothness in the presence of depth discontinuities, we incorporate disparity gradient information as a gradient regularizer into the loss function to preserve local structure details in large depth discontinuity areas. We evaluate our model in terms of end-point-error on several challenging stereo datasets including Scene Flow, Sintel and KITTI. Experimental results demonstrate that our model decreases the estimation error compared with DispNet on most datasets (e.g. we obtain an improvement of 46% on Sintel) and estimates better structure-preserving disparity maps. Moreover, our proposal also achieves competitive performance compared to other methods. Numéro de notice : A2019-496 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.012 date de publication en ligne : 27/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.012 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93729 [article]

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Detecting and mapping traffic signs from Google Street View images using deep learning and GIS / Andrew Campbell in Computers, Environment and Urban Systems, vol 77 (september 2019)  

Public [article]  inComputers, Environment and Urban Systems > vol 77 (september 2019) Titre : Detecting and mapping traffic signs from Google Street View images using deep learning and GIS Type de document : Article/Communication Auteurs : Andrew Campbell, Auteur ; Alan Both, Auteur ; Qian (Chayn) Sun, Auteur ; Qian (Chayn) Sun, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données routières [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] feu de circulation [Termes descripteurs IGN] gestion de trafic [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] signalisation routière [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] vision par ordinateur Résumé : (auteur) Street traffic sign infrastructure remains an extremely difficult asset for local government to manage due to its diverse physical structure and geographical distribution. A spatial registrar of traffic infrastructure is currently a required component of local government councils' mandatory road management plans. Recent advancements of object detection technology in machine learning have presented an automated approach for the detection and classification of street signage captured by Google's Street View (GSV) imagery. This paper explores the possibility of using deep learning to produce an autonomous system for detecting traffic signs on GSV images to assist in traffic assets monitoring and maintenance. By leveraging Google's Street View API, this research offers an economic approach of building purposeful street sign computer vision datasets. A custom object detection model was trained to detect and classify Stop and Give Way signs from images captured at intersection approaches. Considering the output detected bounding box coordinates, photogrammetry approach was applied to calculate the approximate location of each detected sign in two-dimensional geographical space. The newly located and classified street signs can be combined with relevant spatial data for implementation into an asset management system. By combining GIS and the GSV API, the process is completely scalable to any level of street sign classification scope. The experiments conducted on the road network of study area recorded a detection accuracy of 95.63% and classification accuracy of 97.82%. Our proposed automated approach to the detection and localisation of street sign infrastructure has displayed a promising potential for its use by local government authorities. Our workflow can be used to detect other traffic signs and applied to other road sections and other cities. Of primary importance, this approach takes an entirely free and open-source approach throughout. The continuation of Google's Street View program will account for the spatiotemporal representation of street sign infrastructure for the ongoing maintenance and renewal programs of this valuable asset. Numéro de notice : A2019-412 Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2019.101350 date de publication en ligne : 07/06/2019 En ligne : https://doi.org/10.1016/j.compenvurbsys.2019.101350 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93536 [article]

Learning scene geometry for visual localization in challenging conditions / Nathan Piasco (2019)    

Public Titre : Learning scene geometry for visual localization in challenging conditions Type de document : Article/Communication Auteurs : Nathan Piasco , Auteur ; Désiré Sidibé, Auteur ; Valérie Gouet-Brunet , Auteur ; Cédric Demonceaux, Auteur Congrès : ICRA 2019, International Conference on Robotics and Automation (20 - 24 mai 2019; Montréal, Québec - Canada) , Commanditaire Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2019 Projets : PLaTINUM / Gouet-Brunet, Valérie Importance : pp 9094 - 9100 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] analyse visuelle [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] carte de profondeur [Termes descripteurs IGN] descripteur [Termes descripteurs IGN] géométrie de l'image [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] localisation basée vision [Termes descripteurs IGN] précision de localisation [Termes descripteurs IGN] prise de vue nocturne [Termes descripteurs IGN] robotique [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] variation diurne [Termes descripteurs IGN] variation saisonnière [Termes descripteurs IGN] vision par ordinateur Résumé : (auteur) We propose a new approach for outdoor large scale image based localization that can deal with challenging scenarios like cross-season, cross-weather, day/night and longterm localization. The key component of our method is a new learned global image descriptor, that can effectively benefit from scene geometry information during training. At test time, our system is capable of inferring the depth map related to the query image and use it to increase localization accuracy. We are able to increase recall@1 performances by 2.15% on cross-weather and long-term localization scenario and by 4.24% points on a challenging winter/summer localization sequence versus state-of-the-art methods. Our method can also use weakly annotated data to localize night images across a reference dataset of daytime images. Numéro de notice : C2019-002 Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICRA.2019.8794221 date de publication en ligne : 12/08/2019 En ligne : http://doi.org/10.1109/ICRA.2019.8794221 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93774

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