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Object recognition algorithm based on optimized nonlinear activation function-global convolutional neural network / Feng-Ping An in The Visual Computer, vol 38 n° 2 (February 2022)
[article]
Titre : Object recognition algorithm based on optimized nonlinear activation function-global convolutional neural network Type de document : Article/Communication Auteurs : Feng-Ping An, Auteur ; Jun-e Liu, Auteur ; Lei Bai, Auteur Année de publication : 2022 Article en page(s) : pp 541 - 553 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] détection d'objet
[Termes IGN] programmation non linéaire
[Termes IGN] réseau neuronal convolutifRésumé : (auteur) Traditional object recognition algorithms cannot meet the requirements of object recognition accuracy in the actual warehousing and logistics field. In recent years, the rapid development of the deep learning theory has provided a technical approach for solving the above problems, and a number of object recognition algorithms has been proposed based on deep learning, which have been promoted and applied. However, deep learning has the following problems in the application process of object recognition: First, the nonlinear modeling ability of the activation function in the deep learning model is poor; second, the deep learning model has a large number of repeated pooling operations during which information is lost. In view of these shortcomings, this paper proposes multiple-parameter exponential linear units with uniform and learnable parameter forms and introduces two learned parameters in the exponential linear unit (ELU), enabling it to represent piecewise linear and exponential nonlinear functions. Therefore, the ELU has good nonlinear modeling capabilities. At the same time, to improve the problem of losing information in the large number of repeated pooling operations, this paper proposes a new global convolutional neural network structure. This network structure makes full use of the local and global information of different layer feature maps in the network. It can reduce the problem of losing feature information in the large number of pooling operations. Based on the above ideas, this paper suggests an object recognition algorithm based on the optimized nonlinear activation function-global convolutional neural network. Experiments were carried out on the CIFAR100 dataset and the ImageNet dataset using the object recognition algorithm proposed in this paper. The results show that the object recognition method suggested in this paper not only has a better recognition accuracy than traditional machine learning and other deep learning models but also has a good stability and robustness. Numéro de notice : A2022-147 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-020-02033-x Date de publication en ligne : 03/01/2022 En ligne : https://doi.org/10.1007/s00371-020-02033-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100041
in The Visual Computer > vol 38 n° 2 (February 2022) . - pp 541 - 553[article]Recurrent origin–destination network for exploration of human periodic collective dynamics / Xiaojian Chen in Transactions in GIS, vol 26 n° 1 (February 2022)
[article]
Titre : Recurrent origin–destination network for exploration of human periodic collective dynamics Type de document : Article/Communication Auteurs : Xiaojian Chen, Auteur ; Jiayi Xie, Auteur ; Changjiang Xiao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 317 - 340 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] données localisées dynamiques
[Termes IGN] flux
[Termes IGN] origine - destination
[Termes IGN] planification urbaine
[Termes IGN] réseau neuronal récurrent
[Termes IGN] série temporelle
[Termes IGN] taxi
[Termes IGN] Wuhan (Chine)Résumé : (auteur) While daily periodic movements of individuals have been widely studied, their collective dynamics are not understood. To capture periodic collective dynamics, this article represents individual daily movements as a time series of directed weighted origin–destination (OD) networks, and proposes an approach to identify a sub-network called the “recurrent OD network”, which contains frequent edges appearing in each day. Taxi trajectory data over a period of 6 months in Wuhan, China are used for the case study. Here, we extracted the recurrent OD networks for each 2-h period on a given day, and compared them with the corresponding “major OD network” defined by both frequent and infrequent edges. Results show that the recurrent OD networks coincidentally exhibit spatially localized community structures and distinctive patterns of inflow and outflow for each region within a day. Overall, both methodology and findings in this study might make significant contributions in a range of fields, such as urban planning, regional economic development, and infectious disease control. Numéro de notice : A2022-179 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12849 Date de publication en ligne : 05/10/2021 En ligne : https://doi.org/10.1111/tgis.12849 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99838
in Transactions in GIS > vol 26 n° 1 (February 2022) . - pp 317 - 340[article]Spatiotemporal temperature fusion based on a deep convolutional network / Xuehan Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)
[article]
Titre : Spatiotemporal temperature fusion based on a deep convolutional network Type de document : Article/Communication Auteurs : Xuehan Wang, Auteur ; Zhenfeng Shao, Auteur ; Xiao Huang, Auteur ; Deren Li, Auteur Année de publication : 2022 Article en page(s) : pp 93 - 101 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] données spatiotemporelles
[Termes IGN] fusion de données multisource
[Termes IGN] image Landsat
[Termes IGN] image Terra-MODIS
[Termes IGN] réseau neuronal convolutif
[Termes IGN] série temporelle
[Termes IGN] température au sol
[Termes IGN] température de surfaceRésumé : (Auteur) High-spatiotemporal-resolution land surface temperature (LST) images are essential in various fields of study. However, due to technical constraints, sensing systems have difficulty in providing LSTs with both high spatial and high temporal resolution. In this study, we propose a multi-scale spatiotemporal temperature-image fusion network (MSTTIFN) to generate high-spatial-resolution LST products. The MSTTIFN builds nonlinear mappings between the input Moderate Resolution Imaging Spectroradiometer (MODIS) LSTs and the out- put Landsat LSTs at the target date with two pairs of references and therefore enhances the resolution of time-series LSTs. We conduct experiments on the actual Landsat and MODIS data in two study areas (Beijing and Shandong) and compare our proposed MSTTIFN with four competing methods: the Spatial and Temporal Adaptive Reflectance Fusion Model, the Flexible Spatiotemporal Data Fusion Model, a two-stream convolutional neural network (StfNet), and a deep learning-based spatiotemporal temperature-fusion network. Results reveal that the MSTTIFN achieves the best and most stable performance. Numéro de notice : A2022-064 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00023R2 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.14358/PERS.21-00023R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99724
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 2 (February 2022) . - pp 93 - 101[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2022021 SL Revue Centre de documentation Revues en salle Disponible Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images / Alireza Hamedianfar in Geocarto international, vol 37 n° 3 ([01/02/2022])
[article]
Titre : Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images Type de document : Article/Communication Auteurs : Alireza Hamedianfar, Auteur ; Mohamed Barakat A. Gibril, Auteur ; Mohammadjavad Hosseinpoor, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 773 - 791 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] carte d'occupation du sol
[Termes IGN] Extreme Gradient Machine
[Termes IGN] image à très haute résolution
[Termes IGN] image Worldview
[Termes IGN] itération
[Termes IGN] optimisation (mathématiques)
[Termes IGN] optimisation par essaim de particules
[Termes IGN] réseau neuronal artificiel
[Termes IGN] segmentation d'image
[Termes IGN] zone urbaineRésumé : (auteur) Geographic object-based image analysis (GEOBIA) has emerged as an effective and evolving paradigm for analyzing very high resolution (VHR) images as it demonstrates preeminence over the traditional pixel-wise methods and enables the utilization of diverse spectral, geometrical, and textural information to for image classification. Among feature selection (FS) methods, metaheuristic FS techniques have recently demonstrated effective performance in the dimensionality reduction of GEOBIA features. In this study, an artificial neural network (ANN) was integrated with particle swarm optimization (PSO) to enhance the learning process and more effectively determine the most significant features and their importance using WorldView-3 (WV-3) satellite data. First, multi-resolution image segmentation parameters were tuned using Taguchi optimization technique and unsupervised segmentation quality measure. Second, the proposed ANN–PSO was compared with PSO under 100 iterations. The ANN–PSO integration achieved lower root mean square error (RMSE) in all the iterations. Third, state-of-the-art extreme gradient boosting (Xgboost) image classifier was used to derive the land use/land cover (LULC) map of the first study area and assess the transferability of the selected features on the second and third regions. The Xgboost classifier obtained 91.68%, 89.54%, and 89.33% overall accuracies for the first, second, and third sites, respectively. ANN contributed to an intelligent approach for identifying which features are more likely to be relevant and discriminate the land cover types. The proposed integrated FS is a promising approach and an efficient tool for determining significant features and enhancing the detection of urban LULC classes from WV-3 data. Numéro de notice : A2022-344 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1737974 Date de publication en ligne : 12/03/2020 En ligne : https://doi.org/10.1080/10106049.2020.1737974 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100525
in Geocarto international > vol 37 n° 3 [01/02/2022] . - pp 773 - 791[article]Analysis of pedestrian movements and gestures using an on-board camera to predict their intentions / Joseph Gesnouin (2022)
Titre : Analysis of pedestrian movements and gestures using an on-board camera to predict their intentions Titre original : Analyse des mouvements et gestes des piétons via caméra embarquée pour la prédiction de leurs intentions Type de document : Thèse/HDR Auteurs : Joseph Gesnouin, Auteur ; Fabien Moutarde, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2022 Importance : 171 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de doctorat de l'Université Paris Sciences et Lettres, Préparée à MINES ParisTech, Spécialité
Informatique temps réel, robotique et automatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] estimation de pose
[Termes IGN] image RVB
[Termes IGN] instrument embarqué
[Termes IGN] navigation autonome
[Termes IGN] piéton
[Termes IGN] reconnaissance de gestes
[Termes IGN] réseau neuronal de graphes
[Termes IGN] squelettisation
[Termes IGN] trajectoire (véhicule non spatial)
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The autonomous vehicle (AV) is a major challenge for the mobility of tomorrow. Progress is being made every day to achieve it; however, many problems remain to be solved to achieve a safe outcome for the most vulnerable road users (VRUs). One of the major challenge faced by AVs is the ability to efficiently drive in urban environments. Such a task requires interactions between autonomous vehicles and VRUs to resolve traffic ambiguities. In order to interact with VRUs, AVs must be able to understand their intentions and predict their incoming actions. In this dissertation, our work revolves around machine learning technology as a way to understand and predict human behaviour from visual signals and more specifically pose kinematics. Our goal is to propose an assistance system to the AV that is lightweight, scene-agnostic that could be easily implemented in any embedded devices with real-time constraints. Firstly, in the gesture and action recognition domain, we study and introduce different representations for pose kinematics, based on deep learning models as a way to efficiently leverage their spatial and temporal components while staying in an euclidean grid-space. Secondly, in the autonomous driving domain, we show that it is possible to link the posture, the walking attitude and the future behaviours of the protagonists of a scene without using the contextual information of the scene (zebra crossing, traffic light...). This allowed us to divide by a factor of 20 the inference speed of existing approaches for pedestrian intention prediction while keeping the same prediction robustness. Finally, we assess the generalization capabilities of pedestrian crossing predictors and show that the classical train-test sets evaluation for pedestrian crossing prediction, i.e., models being trained and tested on the same dataset, is not sufficient to efficiently compare nor conclude anything about their applicability in a real-world scenario. To make the research field more sustainable and representative of the real advances to come. We propose new protocols and metrics based on uncertainty estimates under domain-shift in order to reach the end-goal of pedestrian crossing behavior predictors: vehicle implementation. Note de contenu : 1- Introduction
2- Human activity recognition with pose-driven deep learning models
3- From action recognition to pedestrian discrete intention prediction
4- Assessing the generalization of pedestrian crossing predictors
5- ConclusionNuméro de notice : 24066 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique temps réel, robotique et automatique : Paris Sciences et Lettres : 2022 DOI : sans En ligne : https://tel.hal.science/tel-03813520 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102091 Apprentissage de représentations et modèles génératifs profonds dans les systèmes dynamiques / Jean-Yves Franceschi (2022)PermalinkConstruction d’un plugin QGIS de détection d’îlots de chaleur urbains à partir d’images satellitaires de type optique / Houssayn Meriche (2022)PermalinkContribution to object extraction in cartography : A novel deep learning-based solution to recognise, segment and post-process the road transport network as a continuous geospatial element in high-resolution aerial orthoimagery / Calimanut-Ionut Cira (2022)PermalinkPermalinkPermalinkDétection des prairies de fauche et estimation des périodes de fauche par télédétection / Emma Seneschal (2022)PermalinkDeveloping the potential of airborne lidar systems for the sustainable management of forests / Karun Dayal (2022)PermalinkEstimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data / Fardin Moradi in Forests, vol 13 n° 1 (January 2022)PermalinkA GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods / Pengxiang Zhao in Remote sensing, vol 14 n° 1 (January-1 2022)PermalinkInteractive semantic segmentation of aerial images with deep neural networks / Gaston Lenczner (2022)Permalink