Centre dedocumentation
scientifique

Characteristics of augmented map research from a cartographic perspective / Yi Cheng in Cartography and Geographic Information Science, Vol 49 n° 5 (September 2022)  

Public [article]  inCartography and Geographic Information Science > Vol 49 n° 5 (September 2022) . - pp 426 - 442 Titre : Characteristics of augmented map research from a cartographic perspective Type de document : Article/Communication Auteurs : Yi Cheng, Auteur ; Guochuang Zhu, Auteur ; Cong Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 426 - 442 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte interactive [Termes IGN] cognition [Termes IGN] compréhension de l'image [Termes IGN] lecture de carte [Termes IGN] réalité augmentée [Termes IGN] représentation cartographique 3D [Termes IGN] visualisation cartographique [Vedettes matières IGN] Cartologie Résumé : (auteur) “Why,” “what,” and “how” are basic questions to be answered in augmented map research as an intersecting direction. This study summarizes dynamic visual representations and true 3D interactions as characteristics of augmented maps from the cartographic perspective through analysis of the research progress in different disciplines. From secondary viewpoints of cognition and design, the augmented map cube is presented to establish the research framework in three directions: cognitive purposes, information dimensions, and interactive devices, in which map-based spatial cognition theory, augmented visualization, and interactive features are considered. The research evaluation is carried out to determine the reasonableness of the cube and then identify different research statuses in any one or two of the directions under the cube. Based on a literature search and classification, 30 typical studies were used for structural analysis to discover research trends and new directions that can be mined. The results show that the cube can be used to evaluate the coverage of an article or provide researchers with research trends and new possibilities. Our conclusions include but are not limited to the following: Research for retrieval purposes deserves attention, augmented visualization of specific individual elements is key to understanding maps, and interactive devices become more intangible. Numéro de notice : A2022-634 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2022.2059571 Date de publication en ligne : 22/04/2022 En ligne : https://doi.org/10.1080/15230406.2022.2059571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101401 [article]

Scene understanding and gesture recognition for human-machine interaction / Naina Dhingra (2022)  

Public Titre : Scene understanding and gesture recognition for human-machine interaction Type de document : Thèse/HDR Auteurs : Naina Dhingra, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2022 Note générale : Bibliographie A dissertation submitted to attain the degree of Doctor of Sciences of ETH Zurich Langues : Français (fre) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification orientée objet [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] compréhension de l'image [Termes IGN] image RVB [Termes IGN] interaction homme-machine [Termes IGN] oculométrie [Termes IGN] reconnaissance automatique [Termes IGN] reconnaissance de formes [Termes IGN] reconnaissance de gestes [Termes IGN] réseau neuronal récurrent [Termes IGN] scène [Termes IGN] vision par ordinateur Résumé : (auteur) Scene understanding and gesture recognition are useful for a myriad of applications such as human-robotic interaction, assisting blind and visually impaired people, advanced driver assistance systems, and autonomous driving. To work autonomously in real-world environments, automatic systems need to deliver non-verbal information to enhance the verbal communication in particular for blind people. We are exploring the holistic approach for providing the scene as well as gesture related information. We propose that incorporating attention mechanisms in neural networks which behave similarly to attention in the human brain, and conducting an integrated study using neural networks in real-time can yield significant improvements in the scene and gesture understanding, thereby enhancing the user experience. In this thesis, we investigate the understanding of visual scenes and gestures. We explore these two areas, in particular, by proposing novel architectures, training methods, user studies, and thorough evaluations. We show that, for deep learning approaches, attention or self attention mechanisms improve and push the boundaries of network performance for different tasks in consideration. We suggest that the various kinds of gestures can complement and supplement each other’s information to better understand non-verbal conversation; hence integrated gestures comprehension is useful. First, we focus on visual scene understanding using scene graph generation. We propose, BGT-Net, a new network that uses an object detection model with 1) bidirectional gated recurrent units for object-object communication and 2) transformer encoders including self attention to classify the objects and their relationships. We address the problem of bias caused by the long tailed distribution in the dataset. This enables the network to perform even for the unseen objects or relationships in the dataset. Second, we propose to learn hand gesture recognition from RGB and RGB-D videos using attention learning. We present a novel architecture based on residual connections and an attention mechanism. Our approach successfully detects hand gestures when evaluated on three open-source datasets. Third, we explore pointing gesture recognition and localization using open-source software, i.e. OpenPtrack which uses a deep learning based iii network to track multi-persons in the scene. We use a Kinect sensor as an input device and conduct a user study with 26 users to evaluate the system using two setup types. Fourth, we propose a technique to perform eye gaze tracking using OpenFace which is based on a deep learning model and RGB webcam. We use support vector machine regression to estimate the position of eye gaze on the screen. In a study, we evaluate the system with 28 users and show that this system can perform similarly to commercially expensive eye trackers. Finally, we focus on 3D head pose estimation using two models: 1)headPosr includes residual connections for the base network followed by a transformer encoder. It outperforms existing models but has a drawback of being computationally expensive; 2) lwPosr uses depthwise separable convolutions and transformer encoders. It is a two stream network in fine-grained fashion to estimate the three angles of the head pose. We demonstrate that this method is able to predict head poses better than state-of-the-art lightweight networks. Note de contenu : 1- Introduction 2- Background 3- State of the art 4- Scene graph generation 5- 3D hand gesture recognition 6- Pointing gesture recognition 7- Eye-gaze tracking 8- Head pose estimation 9- Lightweight head pose estimation 10- Summary Numéro de notice : 24039 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD Thesis : Sciences : ETH Zurich :2022 DOI : sans En ligne : https://www.research-collection.ethz.ch/handle/20.500.11850/559347 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101876

Evaluating narrative in geoportals for territorial public policies / Luis Manuel Batista in Cartographica, vol 56 n° 4 (Winter 2021)  

Public [article]  inCartographica > vol 56 n° 4 (Winter 2021) . - pp 303 - 319 Titre : Evaluating narrative in geoportals for territorial public policies Type de document : Article/Communication Auteurs : Luis Manuel Batista, Auteur ; Ana Figueiras, Auteur Année de publication : 2021 Article en page(s) : pp 303 - 319 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse comparative [Termes IGN] carte thématique [Termes IGN] collectivité territoriale [Termes IGN] communication cartographique [Termes IGN] compréhension de l'image [Termes IGN] conception cartographique [Termes IGN] géoportail [Termes IGN] plan local d'urbanisme [Termes IGN] politique territoriale [Termes IGN] Portugal [Termes IGN] web mapping Résumé : (auteur) To do territorial planning, we need several maps referring to different layers of information necessary to represent the territory according to a vast set of variables. At the Portuguese municipal level, the municipal master plan (PDM – plano diretor municipal) is the territorial management tool responsible for long-term territorial planning and ordering. Since the PDM will constrain the citizens’ lives, it should be of easy access and interpretation. However, due to its large amount of information, it is often hard for them to understand what is presented using only static cartographic elements. Comparing 60 Web sites that present geographical information for a wider public, we found that narrative, visual, and interactive elements and data presentation in flexible portions with the necessary information for partitioning, browsing, or querying made the data more engaging. This flexibility is particularly important with large data sets, where decreasing the level of complexity is vital for proper understanding and analysis. We found that geoportals dedicated to territorial public policies lack narrative elements that would improve comprehension. Since it is difficult for the general public to understand the strategy the municipality wants to implement, the desired public participation, although possible, will return poor results. Numéro de notice : A2021-887 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3138/cart-2021-0023 Date de publication en ligne : 02/12/2021 En ligne : https://doi.org/10.3138/cart-2021-0023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99227 [article]

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
031-2021041	SL	Revue	Centre de documentation	Revues en salle	Disponible
031-2021042	SL	Revue	Centre de documentation	Revues en salle	Disponible

Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification / Ming Cong in Geocarto international, vol 36 n° 18 ([01/10/2021])  

Public [article]  inGeocarto international > vol 36 n° 18 [01/10/2021] . - pp 2065 - 2084 Titre : Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification Type de document : Article/Communication Auteurs : Ming Cong, Auteur ; Zhiye Wang, Auteur ; Yiting Tao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2065 - 2084 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] chromatopsie [Termes IGN] classification non dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compréhension de l'image [Termes IGN] échantillonnage d'image [Termes IGN] filtrage numérique d'image [Termes IGN] image captée par drone [Termes IGN] vision [Termes IGN] vision par ordinateur Résumé : (auteur) Unmanned aerial vehicle remote sensing images need to be precisely and efficiently classified. However, complex ground scenes produced by ultra-high ground resolution, data uniqueness caused by multi-perspective observations, and need for manual labelling make it difficult for current popular deep learning networks to obtain reliable references from heterogeneous samples. To address these problems, this paper proposes an optic nerve microsaccade (ONMS) classification network, developed based on multiple dilated convolution. ONMS first applies a Laplacian of Gaussian filter to find typical features of ground objects and establishes class labels using adaptive clustering. Then, using an image pyramid, multi-scale image data are mapped to the class labels adaptively to generate homologous reliable samples. Finally, an end-to-end multi-scale neural network is applied for classification. Experimental results show that ONMS significantly reduces sample labelling costs while retaining high cognitive performance, classification accuracy, and noise resistance—indicating that it has significant application advantages. Numéro de notice : A2021-707 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2019.1687593 Date de publication en ligne : 07/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1687593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98602 [article]

Semantic hierarchy emerges in deep generative representations for scene synthesis / Ceyuan Yang in International journal of computer vision, vol 129 n° 5 (May 2021)  

Public [article]  inInternational journal of computer vision > vol 129 n° 5 (May 2021) . - pp 1451 - 1466 Titre : Semantic hierarchy emerges in deep generative representations for scene synthesis Type de document : Article/Communication Auteurs : Ceyuan Yang, Auteur ; Yujun Shen, Auteur ; Bolei Zhou, Auteur Année de publication : 2021 Article en page(s) : pp 1451 - 1466 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse visuelle [Termes IGN] apprentissage profond [Termes IGN] compréhension de l'image [Termes IGN] représentation des connaissances [Termes IGN] réseau antagoniste génératif [Termes IGN] segmentation hiérarchique [Termes IGN] segmentation sémantique [Termes IGN] synthèse d'image Résumé : (auteur) Despite the great success of Generative Adversarial Networks (GANs) in synthesizing images, there lacks enough understanding of how photo-realistic images are generated from the layer-wise stochastic latent codes introduced in recent GANs. In this work, we show that highly-structured semantic hierarchy emerges in the deep generative representations from the state-of-the-art GANs like StyleGAN and BigGAN, trained for scene synthesis. By probing the per-layer representation with a broad set of semantics at different abstraction levels, we manage to quantify the causality between the layer-wise activations and the semantics occurring in the output image. Such a quantification identifies the human-understandable variation factors that can be further used to steer the generation process, such as changing the lighting condition and varying the viewpoint of the scene. Extensive qualitative and quantitative results suggest that the generative representations learned by the GANs with layer-wise latent codes are specialized to synthesize various concepts in a hierarchical manner: the early layers tend to determine the spatial layout, the middle layers control the categorical objects, and the later layers render the scene attributes as well as the color scheme. Identifying such a set of steerable variation factors facilitates high-fidelity scene editing based on well-learned GAN models without any retraining (code and demo video are available at https://genforce.github.io/higan). Numéro de notice : A2021-408 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-020-01429-5 Date de publication en ligne : 10/02/2021 En ligne : https://doi.org/10.1007/s11263-020-01429-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97725 [article]

Recognition of varying size scene images using semantic analysis of deep activation maps / Shikha Gupta in Machine Vision and Applications, vol 32 n° 2 (March 2021)  

Permalink

Spatial multi-criteria evaluation in 3D context: suitability analysis of urban vertical development / Kendra Munn in Cartography and Geographic Information Science, vol 48 n° 2 (March 2021)  

Permalink

Activity recognition in residential spaces with Internet of things devices and thermal imaging / Kshirasagar Naik in Sensors, vol 21 n° 3 (February 2021)  

Permalink

Deep convolutional neural networks for scene understanding and motion planning for self-driving vehicles / Abdelhak Loukkal (2021)  

Permalink

Deep-learning for 3D reconstruction / Fabio Tosi (2021)  

Permalink

Dynamic scene understanding using deep neural networks / Ye Lyu (2021)  

Permalink

From point clouds to high-fidelity models - advanced methods for image-based 3D reconstruction / Audrey Richard (2021)  

Permalink

Slope engineering / Ali Ismet Kanlı (2021)  

Permalink

Unsupervised vision methods based on image perceptual information / Eric Bazan (2021)  

Permalink

X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Permalink