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Attention mechanisms in computer vision: A survey / Meng-Hao Guo in Computational Visual Media, vol 8 n° 3 (September 2022)  

Public [article]  inComputational Visual Media > vol 8 n° 3 (September 2022) . - pp 331 - 368 Titre : Attention mechanisms in computer vision: A survey Type de document : Article/Communication Auteurs : Meng-Hao Guo, Auteur ; Tian-Xing Xu, Auteur ; Jiang-Jiang Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 331 - 368 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] attention (apprentissage automatique) [Termes IGN] saillance [Termes IGN] scène Résumé : (auteur) Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multimodal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository https://github.com/MenghaoGuo/Awesome-Vision-Attentions is dedicated to collecting related work. We also suggest future directions for attention mechanism research. Numéro de notice : A2022-329 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s41095-022-0271-y Date de publication en ligne : 15/03/2022 En ligne : https://doi.org/10.1007/s41095-022-0271-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100601 [article]

An informal road detection neural network for societal impact in developing countries / Inger Fabris-Rotelli in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2022 (2022 edition) . - pp 267 - 274 Titre : An informal road detection neural network for societal impact in developing countries Type de document : Article/Communication Auteurs : Inger Fabris-Rotelli, Auteur ; Abraham Wannenburg, Auteur ; Gao Maribe, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 267 - 274 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] Afrique du sud (état) [Termes IGN] apprentissage profond [Termes IGN] données étiquetées d'entrainement [Termes IGN] extraction du réseau routier [Termes IGN] image satellite [Termes IGN] impact social [Termes IGN] pays en développement [Termes IGN] réseau neuronal artificiel Résumé : (auteur) Roads found in informal settlements arise out of convenience, and are often not recorded or maintained by authorities. This complicates service delivery, sustainable development and crisis mitigation, including management and tracking of COVID-19. We, therefore, aim to extract informal roads in remote sensing images. Existing techniques aiming at the extraction of formal roads are not suitable for the problem due to the complex physical and spectral properties of informal roads. The only existing approaches for informal roads, namely (Nobrega et al., 2006, Thiede et al., 2020), do not consider neural networks as a solution. Neural networks show promise in overcoming these complexities. However, they require a large amount of data to learn, which is currently not available due to the expensive and time-consuming nature of collecting such data. This paper implements a neural network to extract informal roads from a data set digitised by this research group. Data quality is assessed by calculating validity completeness, homogeneity and the V-measure, a measure of consistency, in order to evaluate the overall usability of the dataset for neural network informal road detection. We implement the GANs-UNet model that obtained the highest F1-score in a 2020 review paper (Abdollahi et al., 2020) on the state-of-the-art deep learning models used to extract formal roads. The results indicate that the model is able to extract informal roads successfully in the presence of appropriate training data. Numéro de notice : A2022-424 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-4-2022-267-2022 Date de publication en ligne : 18/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2022-267-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100729 [article]

Exploring scientific literature by textual and image content using DRIFT / Ximena Pocco in Computers and graphics, vol 103 (April 2022)  

Public [article]  inComputers and graphics > vol 103 (April 2022) . - pp 140 - 152 Titre : Exploring scientific literature by textual and image content using DRIFT Type de document : Article/Communication Auteurs : Ximena Pocco, Auteur ; Tiago da Silva, Auteur ; Jorge Poco, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 140 - 152 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse visuelle [Termes IGN] bibliothèque numérique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] corpus [Termes IGN] exploration de données [Termes IGN] extraction de traits caractéristiques [Termes IGN] recherche d'image basée sur le contenu [Termes IGN] recherche scientifique [Termes IGN] similitude sémantique Résumé : (auteur) Digital libraries represent the most valuable resource for storing, querying, and retrieving scientific literature. Traditionally, the reader/analyst aims to compose a set of articles based on keywords, according to his/her preferences, and manually inspect the resulting list of documents. Except for the articles which share citations or common keywords, the results retrieved will be limited to those which fulfill a syntactic match. Besides, if instead of having an article as a reference, the user has an image, the process of finding and exploring articles with similar content becomes infeasible. This paper proposes a visual analytic methodology for exploring and analyzing scientific document collections that consider both textual and image content. The proposed technique relies on combining multiple Content-Based Image Retrieval (CBIR) components and multidimensional projection to map the documents to a visual space based on their similarity, thus enabling an interactive exploration. Moreover, we extend its analytical capabilities with visual resources to display complementary information on selected documents that uncover hidden patterns and semantic relations. We evidence the effectiveness of our methodology through three case studies and a user evaluation, which attest to its usefulness during the process of scientific collections exploration. Numéro de notice : A2022-289 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cag.2022.02.005 Date de publication en ligne : 11/02/2022 En ligne : https://doi.org/10.1016/j.cag.2022.02.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100332 [article]

High-performance adaptive texture streaming and rendering of large 3D cities / Alex Zhang in The Visual Computer, vol 38 n° 4 (April 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 4 (April 2022) Titre : High-performance adaptive texture streaming and rendering of large 3D cities Type de document : Article/Communication Auteurs : Alex Zhang, Auteur ; Kan Chen, Auteur ; Henry Johan, Auteur ; Marius Erdt, Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] couleur à l'écran [Termes IGN] flux continu [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] rendu (géovisualisation) [Termes IGN] texturage [Termes IGN] ville Résumé : (auteur) We propose a high-performance texture streaming system for real-time rendering of large 3D cities with millions of textures. Our main contribution is a texture streaming system that automatically adjusts the streaming workload at runtime based on measured frame latencies, specifically addressing the high memory binding costs of hardware virtual texturing which causes frame rate stuttering. Our system streams textures in parallel with prioritization based on GPU computed mesh perceptibility, and these textures are cached in a sparse partially resident image at runtime without the need for a texture preprocessing step. In addition, we improve rendering quality by minimizing texture pop-in artifacts using a color blending scheme based on mipmap levels. We evaluate our texture streaming system using three structurally distinct datasets with many textures and compared it to a baseline, a game engine, and our prior method. Results show an 8X improvement in rendering performance and 7X improvement in rendering quality compared to the baseline. Numéro de notice : A2022-148 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-021-02152-z Date de publication en ligne : 01/06/2021 En ligne : https://doi.org/10.1007/s00371-021-02152-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100043 [article]

Procedural urban forestry / Till Niese in ACM Transactions on Graphics, TOG, Vol 41 n° 2 (April 2022)  

Public [article]  inACM Transactions on Graphics, TOG > Vol 41 n° 2 (April 2022) . - pp 1 - 18 Titre : Procedural urban forestry Type de document : Article/Communication Auteurs : Till Niese, Auteur ; Sören Pirk,, Auteur ; Matthias Albrecht,, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] arbre urbain [Termes IGN] paysage urbain [Termes IGN] placement automatique des objets [Termes IGN] scène urbaine [Termes IGN] scène virtuelle Résumé : (auteur) The placement of vegetation plays a central role in the realism of virtual scenes. We introduce procedural placement models (PPMs) for vegetation in urban layouts. PPMs are environmentally sensitive to city geometry and allow identifying plausible plant positions based on structural and functional zones in an urban layout. PPMs can either be directly used by defining their parameters or learned from satellite images and land register data. This allows us to populate urban landscapes with complex 3D vegetation and enhance existing approaches for generating urban landscapes. Our framework’s effectiveness is shown through examples of large-scale city scenes and close-ups of individually grown tree models. We validate the results generated with our framework with a perceptual user study and its usability based on urban scene design sessions with expert users. Numéro de notice : A2022-152 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1145/3502220 En ligne : https://doi.org/10.1145/3502220 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100371 [article]

Hierarchical learning with backtracking algorithm based on the visual confusion label tree for large-scale image classification / Yuntao Liu in The Visual Computer, vol 38 n° 3 (March 2022)  

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Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation / Ramazan Unlu in The Visual Computer, vol 38 n° 2 (February 2022)  

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GCN-Denoiser: mesh denoising with graph convolutional networks / Yuefan Shen in ACM Transactions on Graphics, TOG, Vol 41 n° 1 (February 2022)  

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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)  

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A robust nonrigid point set registration framework based on global and intrinsic topological constraints / Guiqiang Yang in The Visual Computer, vol 38 n° 2 (February 2022)  

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Siamese Adversarial Network for image classification of heavy mineral grains / Huizhen Hao in Computers & geosciences, vol 159 (February 2022)  

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the EUROSDR time benchmark for historical aerial images / E.M. Farella (2022)  

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Fast estimation for robust supervised classification with mixture models / Erwan Giry Fouquet in Pattern recognition letters, vol 152 (December 2021)  

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Connecting images through sources: Exploring low-data, heterogeneous instance retrieval / Dimitri Gominski in Remote sensing, vol 13 n° 16 (August-2 2021)  

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Detection of pictorial map objects with convolutional neural networks / Raimund Schnürer in Cartographic journal (the), vol 58 n° 1 (August 2021)  

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