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Investigating the role of image retrieval for visual localization / Martin Humenberger in International journal of computer vision, vol 130 n° 7 (July 2022)  

Public [article]  inInternational journal of computer vision > vol 130 n° 7 (July 2022) . - 1811 - 1836 Titre : Investigating the role of image retrieval for visual localization Type de document : Article/Communication Auteurs : Martin Humenberger, Auteur ; Yohann Cabon, Auteur ; Noé Pion, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : 1811 - 1836 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse visuelle [Termes IGN] base de données d'images [Termes IGN] estimation de pose [Termes IGN] flou [Termes IGN] localisation basée image [Termes IGN] localisation basée vision [Termes IGN] point de repère [Termes IGN] précision de localisation [Termes IGN] Ransac (algorithme) [Termes IGN] réalité de terrain [Termes IGN] structure-from-motion [Termes IGN] vision par ordinateur Résumé : (auteur) Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localization approaches often rely on image retrieval techniques for one of two purposes: (1) provide an approximate pose estimate or (2) determine which parts of the scene are potentially visible in a given query image. It is common practice to use state-of-the-art image retrieval algorithms for both of them. These algorithms are often trained for the goal of retrieving the same landmark under a large range of viewpoint changes which often differs from the requirements of visual localization. In order to investigate the consequences for visual localization, this paper focuses on understanding the role of image retrieval for multiple visual localization paradigms. First, we introduce a novel benchmark setup and compare state-of-the-art retrieval representations on multiple datasets using localization performance as metric. Second, we investigate several definitions of “ground truth” for image retrieval. Using these definitions as upper bounds for the visual localization paradigms, we show that there is still significant room for improvement. Third, using these tools and in-depth analysis, we show that retrieval performance on classical landmark retrieval or place recognition tasks correlates only for some but not all paradigms to localization performance. Finally, we analyze the effects of blur and dynamic scenes in the images. We conclude that there is a need for retrieval approaches specifically designed for localization paradigms. Our benchmark and evaluation protocols are available at https://github.com/naver/kapture-localization. Numéro de notice : A2022-538 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-022-01615-7 Date de publication en ligne : 25/05/2022 En ligne : https://doi.org/10.1007/s11263-022-01615-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101070 [article]

Effective CBIR based on hybrid image features and multilevel approach / D. Latha in Multimedia tools and applications, vol inconnu (March 2022)  

Public [article]  inMultimedia tools and applications > vol inconnu (March 2022) . - pp Titre : Effective CBIR based on hybrid image features and multilevel approach Type de document : Article/Communication Auteurs : D. Latha, Auteur ; A. Geetha, Auteur Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] base de données d'images [Termes IGN] écart type [Termes IGN] espace colorimétrique [Termes IGN] image en couleur [Termes IGN] image RVB [Termes IGN] matrice de co-occurrence [Termes IGN] motif binaire local [Termes IGN] niveau de gris (image) [Termes IGN] observation multiniveaux [Termes IGN] recherche d'image basée sur le contenu [Termes IGN] saturation de la couleur [Termes IGN] texture d'image [Termes IGN] transformation intensité-teinte-saturation Résumé : (auteur) Content based image retrieval (CBIR) process can retrieve images by matching its feature set values. The proposed novel CBIR methodology called Effective CBIR based on hybrid image features and multilevel approach (CBIR_LTP_GLCM) integrates the hybrid features such as color features and texture features, along with multilevel approach. The color features such as mean and standard deviation are adopted in the proposed method to represent the global color properties of an image. This method manipulates the color input-image by processing the Hue, Saturation and Value channels of the HSV color space. This novel work is enriched with the image feature derived from Local Ternary Pattern (LTP) in addition with GLCM. So, the proposed method CBIR_LTP_GLCM is potentially charged with meaningful modifications travelling with color image manipulation and extended image retrieval accuracy with the aid of multilevel approach. The proposed methodology is experimentally compared with the existing recent CBIR versions by using the standard database such as Corel-1 k, and a user contributed database named DB_VEG. Numéro de notice : A2022-291 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11042-022-12588-7 Date de publication en ligne : 30/03/2022 En ligne : https://doi.org/10.1007/s11042-022-12588-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100337 [article]

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

Public [article]  inCartographic journal (the) > vol 58 n° 1 (August 2021) . - pp 50 - 68 Titre : Detection of pictorial map objects with convolutional neural networks Type de document : Article/Communication Auteurs : Raimund Schnürer, Auteur ; René Sieber, Auteur ; Jost Schmid-Lanter, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 50 - 68 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] base de données d'images [Termes IGN] bibliothèque numérique [Termes IGN] carte ancienne [Termes IGN] carte numérique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] données issues des réseaux sociaux [Termes IGN] objet cartographique [Termes IGN] pictogramme Résumé : (auteur) In this work, realistically drawn objects are identified on digital maps by convolutional neural networks. For the first two experiments, 6200 images were retrieved from Pinterest. While alternating image input options, two binary classifiers based on Xception and InceptionResNetV2 were trained to separate maps and pictorial maps. Results showed that the accuracy is 95–97% to distinguish maps from other images, whereas maps with pictorial objects are correctly classified at rates of 87–92%. For a third experiment, bounding boxes of 3200 sailing ships were annotated in historic maps from different digital libraries. Faster R-CNN and RetinaNet were compared to determine the box coordinates, while adjusting anchor scales and examining configurations for small objects. A resulting average precision of 32% was obtained for Faster R-CNN and of 36% for RetinaNet. Research outcomes are relevant for trawling map images on the Internet and for enhancing the advanced search of digital map catalogues. Numéro de notice : A2021-651 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2020.1738112 Date de publication en ligne : 11/09/2020 En ligne : https://doi.org/10.1080/00087041.2020.1738112 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98381 [article]

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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]

Connecting images through time and sources: Introducing low-data, heterogeneous instance retrieval / Dimitri Gominski (2021)  

Public contenu dans ArXiv / Université Cornell (Etats-Unis) (1991)   Titre : Connecting images through time and sources: Introducing low-data, heterogeneous instance retrieval Type de document : Article/Communication Auteurs : Dimitri Gominski , Auteur ; Valérie Gouet-Brunet , Auteur ; Liming Chen, Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2021 Projets : Alegoria / Gouet-Brunet, Valérie Importance : 5 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] base de données d'images [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] descripteur [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données hétérogènes [Termes IGN] exploration de données [Termes IGN] extraction de traits caractéristiques [Termes IGN] image multi sources [Termes IGN] indexation sémantique [Termes IGN] précision de la classification [Termes IGN] recherche d'image basée sur le contenu Résumé : (auteur) With impressive results in applications relying on feature learning, deep learning has also blurred the line between algorithm and data. Pick a training dataset, pick a backbone network for feature extraction, and voilà; this usually works fora variety of use cases. But the underlying hypothesis that there exists a training dataset matching the use case is not alwaysmet. Moreover, the demand for interconnections regardless of the variations of the content calls for increasing generalization and robustness in features. An interesting application characterized by these problematics is the connection of historical and cultural databases of images.Through the seemingly simple task of instance retrieval, wepropose to show that it is not trivial to pick features respondingwell to a panel of variations and semantic content. Introducing anew enhanced version of the ALEGORIA benchmark, we compare descriptors using the detailed annotations. We further give in sights about the core problems in instance retrieval, testing fourstate-of-the-art additional techniques to increase performance. Numéro de notice : P2021-001 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Preprint nature-HAL : Préprint DOI : sans Date de publication en ligne : 21/03/2021 En ligne : https://arxiv.org/pdf/2103.10729.pdf Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97398

How to spatialize geographical iconographic heritage / Emile Blettery (2021)    

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Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation / Roholah Yazdan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

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Multi-modal temporal attention models for crop mapping from satellite time series / Vivien Sainte Fare Garnot (2021)  

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The challenge of robust trait estimates with deep learning on high resolution RGB images / Etienne David (2021)  

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Unifying remote sensing image retrieval and classification with robust fine-tuning / Dimitri Gominski (2021)  

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Privacy-aware visualization of volunteered geographic information (VGI) to analyze spatial activity: A benchmark implementation / Alexander Dunkel in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)  

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A worldwide 3D GCP database inherited from 20 years of massive multi-satellite observations / Laure Chandelier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 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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Automated terrain feature identification from remote sensing imagery: a deep learning approach / Wenwen Li in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

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Challenging deep image descriptors for retrieval in heterogeneous iconographic collections / Dimitri Gominski (2019)  

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