Descripteur
Termes IGN > sciences naturelles > physique > traitement d'image > analyse d'image numérique > analyse d'image orientée objet
analyse d'image orientée objetVoir aussi |
Documents disponibles dans cette catégorie (229)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Détection et localisation d'objets 3D par apprentissage profond en topologie capteur / Pierre Biasutti (2019)
Titre : Détection et localisation d'objets 3D par apprentissage profond en topologie capteur Type de document : Article/Communication Auteurs : Pierre Biasutti , Auteur ; Aurélie Bugeau, Auteur ; Jean-François Aujol, Auteur ; Mathieu Brédif , Auteur Editeur : Saint-Martin-d'Hères : Groupe de Recherche et d'Etude du Traitement du Signal et des Images GRETSI Année de publication : 2019 Projets : 1-Pas de projet / Conférence : GRETSI 2019, colloque du Groupe de Recherche et d'Etude du Traitement du Signal et des Images 26/08/2019 29/08/2019 Lille France OA proceedings Importance : 4 p. Format : 21 x 30 cm Note générale : Bibliographie
Ce travail a bénéficié d’une aide du programme de Recherche et Innovation European Union’s Horizon 2020 au titre de la bourse Marie Skłodowska-Curie (No 777826).Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compréhension de l'image
[Termes IGN] détection d'objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fusion de données
[Termes IGN] image optique
[Termes IGN] scène 3D
[Termes IGN] semis de pointsRésumé : (Auteur) Ce travail présente une nouvelle méthode pour la détection et la localisation d'objets dans des scènes 3D LiDAR acquises par des systèmes de cartographie mobile. Ce problème est généralement traité en discrétisant l'espace 3D en une fine grille de voxels. Nous introduisons une approche alternative ne nécessitant pas de discrétisation. Elle est basée sur la représentation en 2D du nuage de points en topologie capteur (TC). Cette image sert d'entrée à un réseau de neurones convolutionnels qui en extrait les informations 3D des objets. La réprésentation en topologie capteur présentant des ambiguïtés dans le fond de la scène, nous améliorerons les résultats de détection en couplant ce modèle avec un réseau de détection 2D d'objets sur une image optique. Les prédictions des deux réseaux sont finalement fusionnées pour obtenir les détections finales. Numéro de notice : C2019-014 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Poster nature-HAL : Poster-avec-CL DOI : sans En ligne : https://hal.science/hal-02100719v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93269 Documents numériques
en open access
Détection et localisation d'objets 3D... - pdf auteurAdobe Acrobat PDF Earth observation, remote sensing and geoscientific ground investigations for archaeological and heritage research / Deodato Tapete (2019)
Titre : Earth observation, remote sensing and geoscientific ground investigations for archaeological and heritage research Type de document : Monographie Auteurs : Deodato Tapete, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 304 p. ISBN/ISSN/EAN : 978-3-03921-194-4 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Active Microwave Instrumentation Synthetic Aperture Radar
[Termes IGN] analyse d'image orientée objet
[Termes IGN] bande C
[Termes IGN] bande X
[Termes IGN] détection automatique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image captée par drone
[Termes IGN] image Sentinel-MSI
[Termes IGN] modèle numérique de terrain
[Termes IGN] patrimoine archéologique
[Termes IGN] site archéologiqueRésumé : (Auteur) This book collects 15 papers written by renowned scholars from across the globe that showcase the forefront research in Earth observation (EO), remote sensing (RS), and geoscientific ground investigations to study archaeological records and cultural heritage. Archaeologists, anthropologists, geographers, remote sensing, and archaeometry experts share their methodologies relying on a wealth of techniques and data including, but not limited to: very high resolution satellite images from optical and radar space-borne sensors, air-borne surveys, geographic information systems (GIS), archaeological fieldwork, and historical maps.A couple of the contributions highlight the value of noninvasive and nondestructive laboratory analyses (e.g., neutron diffraction) to reconstruct ancient manufacturing technologies, and of geological ground investigations to corroborate hypotheses of historical events that shaped cultural landscapes.Case studies encompass famous UNESCO World Heritage Sites (e.g., the Nasca Lines in Peru), remote and yet-to-discover archaeological areas in tropical forests in central America, European countries, south Asian changing landscapes, and environments which are arid nowadays but were probably full of woody vegetation in the past.Finally, the reader can learn about the state-of-the-art of education initiatives to train site managers in the use of space technologies in support of their activities, and can understand the legal aspects involved in the application of EO and RS to address current challenges of African heritage preservation. Numéro de notice : 26501 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03921-194-4 En ligne : https://doi.org/10.3390/books978-3-03921-194-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97049
Titre : Entropy in image analysis Type de document : Monographie Auteurs : Amelia Carolina Sparavigna, Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 456 p. Format : 17 x 23 cm ISBN/ISSN/EAN : 978-3-03921-093-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte de profondeur
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] cryptage
[Termes IGN] entropie
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion d'images
[Termes IGN] image infrarouge
[Termes IGN] optimisation (mathématiques)
[Termes IGN] recherche d'image basée sur le contenu
[Termes IGN] segmentation d'imageRésumé : (éditeur) Image analysis is a fundamental task for extracting information from images acquired across a range of different devices. Since reliable quantitative results are requested, image analysis requires highly sophisticated numerical and analytical methods--particularly for applications in medicine, security, and remote sensing, where the results of the processing may consist of vitally important data. The contributions to this book provide a good overview of the most important demands and solutions concerning this research area. In particular, the reader will find image analysis applied for feature extraction, encryption and decryption of data, color segmentation, and in the support new technologies. In all the contributions, entropy plays a pivotal role. Note de contenu : 1- An adaptive weight method for image retrieval based multi-feature fusion
2- A New Image Encryption
3- Algorithm Based on Chaos and Secure Hash SHA-256
4- Video summarization for sign languages using the median of entropy of mean frames method
5- Encryption algorithm of multiple-image using mixed image elements and two dimensional chaotic economic map
6- Improved cryptanalysis and enhancements of an image encryption scheme using combined 1D chaotic maps
7- Blind image quality assessment of natural scenes based on entropy differences in the DCT domain
8- A novel multi-exposure image fusion method based on adaptive patch structure
9- An image encryption algorithm based on time-delay and random insertion
10- Uncertainty assessment of hyperspectral image classification: Deep learning vs. random forest
11- Reconstruction of PET images using cross-entropy and field of experts
12- Objective 3D printed surface quality assessment based on entropy of depth maps
13- Non rigid medical image registration using an information theoretic measure based on arimoto entropy with gradient distributions
14- Study on asphalt pavement surface texture degradation using 3-D image processing techniques and entropy theory
15- Entropy and contrast enhancement of infrared thermal images using the multiscale top-hat transform
16- Breaking an image encryption algorithm based on DNA encoding and spatiotemporal chaosNuméro de notice : 28553 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie DOI : 10.3390/books978-3-03921-093-0 En ligne : https://doi.org/10.3390/books978-3-03921-093-0 Format de la ressource électronique : url Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97428 Joint analysis of SAR and optical satellite images time series for grassland event detection / Anatol Garioud (2019)
Titre : Joint analysis of SAR and optical satellite images time series for grassland event detection Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Silvia Valero, Auteur ; Sébastien Giordano , Auteur ; Clément Mallet , Auteur Editeur : Leibniz : Leibniz Institute of Ecological Urban and Regional Development Année de publication : 2019 Conférence : ILUS 2019, 3rd International land use symposium, Land use changes: Trends and projections 04/12/2019 06/12/2019 Paris France programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification par réseau neuronal
[Termes IGN] cohérence des données
[Termes IGN] détection d'événement
[Termes IGN] détection de changement
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Mâcon
[Termes IGN] prairie
[Termes IGN] puits de carboneRésumé : (auteur) Throughout Europe, grasslands are a major component of the landscape comprising 40% of agricultural land. Permanent Grassland (PM) means land used to grow herbaceous forage crops naturally (self-seeded) or through cultivation (sown) and that has not been included in the crop rotation of the holding for five years or more. PM are major ecosystems associated with high biodiversity which provide a wide range of ecosystem services (e.g. carbon sequestration, water quality, flood and erosion control). Grasslands have an important carbon storage capacity which is valuable for climate protection. Different studies have demonstrated that grassland managements such as grazing or mowing can cause significant effects on carbon storage in soils. Identifying and mapping grassland management practices over time can thus have important impact on climate studies. Remote sensing allows a synoptic and regular monitoring through systematic acquisitions of Earth Observation imagery. The emergence of free and easily Sentinel's satellite data provided by the European Copernicus program, offers new possibilities for grassland monitoring. Sentinel-1 (51) and Sentinel-2 (52) missions acquire radar and optical satellite image time series at high temporal resolution and fine spatial resolution. They fully match the requirements both for yearly and real-time monitoring. In this work, we target to jointly exploit both data sources to dynamically detect mowing events (MowEve) on permanent grasslands. Thematic related analysis of the datasets will highlight strengths and weaknesses of both optical and radar imagery. (i) 52 appears efficient for MowEve detection, with significant variations in the vegetation status that can be easily detected in the spectral signal extracted from the time series of images. But the temporal revisit of 52 although nominally 5 days is often reduced even by half due to the frequent cloud cover (ii) SAR images acquisitions being independent of illumination conditions or cloud cover allows for systematic acquisitions and revisit rate of 6 days. Data consistency makes S1 data essential during fast phenomena such as MowEve. Yet, radar data appears very sensitive to soil moisture, precipitations and geometrical properties making interpretation of their time series more challenging. MowEve detection being weakly supervised, the proposed methodology relies on applying traditional change detection strategies on a low-level fused 51 and S2 data representation. Recurrent Neural Networks will be trained to derive yearly or real-time synthetic 52 vegetation indices from both 52 and S1 observations. Furthermore, through attention mechanisms, our proposed RNN architecture will be able to take into account external data (climate, clouds, topography, etc.) so as to dynamically weight at parcel-level the contribution of optical and radar images. Such method will contribute to obtain dense temporal optical profiles without missing data and compatible with MowEve detection. An experimental evaluation will be carried out on a test site covering an area of 110x110 Km in France (Macon region). Object-oriented analysis will be presented based on permanent grasslands derived from the Land Parcel Identification System. The proposed approach will be compared with traditional MowEve methods essentially based on thresholding independently the different modalities. Numéro de notice : C2019-067 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97022
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 Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2019 Projets : PLaTINUM / Gouet-Brunet, Valérie Conférence : ICRA 2019, International Conference on Robotics and Automation 20/05/2019 24/05/2019 Montréal Québec - Canada Proceedings IEEE Importance : pp 9094 - 9100 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse visuelle
[Termes IGN] appariement d'images
[Termes IGN] carte de profondeur
[Termes IGN] descripteur
[Termes IGN] géométrie de l'image
[Termes IGN] image RVB
[Termes IGN] localisation basée vision
[Termes IGN] précision de localisation
[Termes IGN] prise de vue nocturne
[Termes IGN] robotique
[Termes IGN] scène urbaine
[Termes IGN] variation diurne
[Termes IGN] variation saisonnière
[Termes IGN] vision par ordinateurRésumé : (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 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) 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 Documents numériques
en open access
Learning scene geometry... - pdf auteurAdobe Acrobat PDF PermalinkSeeing the past with computers: Experiments with augmented reality and computer vision for history / Kevin Kee (2019)PermalinkVision-based localization with discriminative features from heterogeneous visual data / Nathan Piasco (2019)PermalinkPermalinkRobust vehicle detection in aerial images using bag-of-words and orientation aware scanning / Hailing Zhou in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkChange detection based on stacked generalization system with segmentation constraint / Kun Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)PermalinkCoupling relationship among scale parameter, segmentation accuracy, and classification accuracy in GeOBIA / Ming Dongping in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)PermalinkMulti-scale object detection in remote sensing imagery with convolutional neural networks / Zhipeng Deng in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)PermalinkAutomated extraction of 3D vector topographic feature line from terrain point cloud / Wei Zhou in Geocarto international, vol 33 n° 10 (October 2018)PermalinkNovel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000 / Umut Gunes Sefercik in Geocarto international, vol 33 n° 10 (October 2018)PermalinkObject-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier / Huanxue Zhang in Geocarto international, vol 33 n° 10 (October 2018)PermalinkStand age estimation of rubber (Hevea brasiliensis) plantations using an integrated pixel- and object-based tree growth model and annual Landsat time series / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)PermalinkAssessment of Nigeriasat-1 satellite data for urban land use/land cover analysis using object-based image analysis in Abuja, Nigeria / Christopher Ifechukwude Chima in Geocarto international, vol 33 n° 9 (September 2018)PermalinkAugmented reality meets computer vision : efficient data generation for urban driving scenes / Hassan Abu Alhaija in International journal of computer vision, vol 126 n° 9 (September 2018)PermalinkAdaptive correlation filters with long-term and short-term memory for object tracking / Chao Ma in International journal of computer vision, vol 126 n° 8 (August 2018)PermalinkDetecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques / Chinsu Lin in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)PermalinkLabel propagation with ensemble of pairwise geometric relations : towards robust large-scale retrieval of object instances / Xiaomeng Wu in International journal of computer vision, vol 126 n° 7 (July 2018)PermalinkA light and faster regional convolutional neural network for object detection in optical remote sensing images / Peng Ding in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)PermalinkPredicting foreground object ambiguity and efficiently crowdsourcing the segmentation(s) / Danna Gurari in International journal of computer vision, vol 126 n° 7 (July 2018)PermalinkA review of accuracy assesment for object-based image analysis: from per pixel to per-polygon approaches [review article] / Su Ye in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)Permalink