Descripteur
Documents disponibles dans cette catégorie (38)
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
Titre : Intelligent Imaging and Analysis Type de document : Monographie Auteurs : DaeEun Kim, Éditeur scientifique ; Dosik Hwang, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 492 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03921-921-6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] estimation de pose
[Termes IGN] image 3D
[Termes IGN] image captée par drone
[Termes IGN] imagerie médicale
[Termes IGN] reconstruction d'image
[Termes IGN] segmentation d'image
[Termes IGN] texture d'image
[Termes IGN] vision par ordinateurRésumé : (éditeur) Imaging and analysis are widely involved in various research fields, including biomedical applications, medical imaging and diagnosis, computer vision, autonomous driving, and robot controls. Imaging and analysis are now facing big changes regarding intelligence, due to the breakthroughs of artificial intelligence techniques, including deep learning. Many difficulties in image generation, reconstruction, de-noising skills, artifact removal, segmentation, detection, and control tasks are being overcome with the help of advanced artificial intelligence approaches. This Special Issue focuses on the latest developments of learning-based intelligent imaging techniques and subsequent analyses, which include photographic imaging, medical imaging, detection, segmentation, medical diagnosis, computer vision, and vision-based robot control. These latest technological developments will be shared through this Special Issue for the various researchers who are involved with imaging itself, or are using image data and analysis for their own specific purposes. Note de contenu : 1- Special features on intelligent imaging and analysis
2- Intelligent evaluation of strabismus in videos based on an automated cover test
3- Application of a real-time visualization method of AUVs in underwater visual localization
4- Volumetric tooth wear measurement of scraper conveyor sprocket using shape from
focus-based method
5- A novel self-intersection penalty term for statistical body shape models and its applications in 3D pose estimation
6- A CNN model for human parsing based on capacity optimization
7- Fast 3D semantic mapping in road scenes †
8- Automated classification analysis of geological structures based on images data and deep learning model
9- Dark spot detection in SAR images of oil spill using segnet
10- A high-resolution texture mapping technique for 3D textured model
11- Image super-resolution algorithm based on dual-channel convolutional neural networks
12- No-reference automatic quality assessment for colorfulness-adjusted, contrast-adjusted, and sharpness-adjusted images using high-dynamic-range-derived features
13- A novel one-camera-five-mirror three-dimensional imaging method for reconstructing the cavitation bubble cluster in a water hydraulic valve
14- Deep residual network with sparse feedback for image restoration
15- An image segmentation method using an active contour model based on improved SPF
and LIF
16- Image segmentation approaches for weld pool monitoring during robotic arc welding
17- A novel discriminating and relative global spatial image representation with applications in CBIR
18- Double low-rank and sparse decomposition for surface defect segmentation of steel sheet
19- A UAV-based visual inspection method for rail surface defects
20- Feature-learning-based printed circuit board inspection via speeded-up robust features and random forest
21- Research progress of visual inspection technology of steel products
22- Fine-grain segmentation of the intervertebral discs from MR spine images using deep convolutional neural networks: BSU-Net
23- Semi-automatic segmentation of vertebral bodies in MR images of human lumbar spinesNuméro de notice : 28500 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie DOI : 10.3390/books978-3-03921-921-6 En ligne : https://doi.org/10.3390/books978-3-03921-921-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96897 Probabilistic pose estimation and 3D reconstruction of vehicles from stereo images / Maximilian Alexander Coenen (2020)
Titre : Probabilistic pose estimation and 3D reconstruction of vehicles from stereo images Type de document : Thèse/HDR Auteurs : Maximilian Alexander Coenen, Auteur Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2020 Collection : DGK - C, ISSN 0065-5325 num. 857 Importance : 160 p. ISBN/ISSN/EAN : 978-3-7696-5269-7 Note générale : bibliographie
Diese Arbeit ist gleichzeitig veröffentlicht in: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Universität HannoverLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] estimation de pose
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle stochastique
[Termes IGN] reconstruction 3D
[Termes IGN] véhicule automobileRésumé : (auteur) The pose estimation and reconstruction of 3D objects from images is one of the major problems that are addressed in computer vision and photogrammetry. The understanding of a 3D scene and the 3D reconstruction of specific objects are prerequisites for many highly relevant applications of computer vision such as mobile robotics and autonomous driving. To deal with the inverse problem of reconstructing 3D objects from their 2D projections, a common strategy is to incorporate prior object knowledge into the reconstruction approach by establishing a 3D model and aligning it to the 2D image plane. However, current approaches are limited due to inadequate shape priors and the insufficiency of the derived image observations for a reliable association and alignment with the 3D model. The goal of this thesis is to infer valuable observations from the images and to show how 3D object reconstruction can profit from a more sophisticated shape prior and from a combined incorporation of the different observation types. To achieve this goal, this thesis presents three major contributions for the particular task of 3Dvehicle reconstruction from street-level stereo images. First, a subcategory-aware deformable vehicle model is introduced that makes use of a prediction of the vehicle type for a more appropriate regularisation of the vehicle shape. Second, a Convolutional Neural Network (CNN) is proposed which extracts observations from an image. In particular, the CNN is used to derive a prediction of the vehicle orientation and type, which are introduced as prior information for model fitting. Furthermore, the CNN extracts vehicle key points and wireframes, which are well-suited for model association and model fitting. Third, the task of pose estimation and reconstruction is addressed by a versatile probabilistic model. Suitable parametrisations and formulations of likelihood and prior terms are introduced for a joint consideration of the derived observations and prior information in the probabilistic objective function. As the objective function is non-convex and discontinuous, a proper customized strategy based on stochastic sampling is proposed for inference, yielding convincing results for the estimated poses and shapes of the vehicles. To evaluate the performance and to investigate the strengths and limitations of the proposed method, extensive experiments are conducted using two challenging real-world data sets: the publicly available KITTI benchmark and the ICSENS data set, which was created in the scope of this thesis. On both data sets, the benefit of the developed shape prior and of each of the individual components of the probabilistic model can be shown. The proposed method yields vehicle pose estimates with a median error of up to 27 cm for the position and up to 1.7◦for the orientation on the data sets. A comparison to state-of-the-art methods for vehicle pose estimation shows that the proposed approach performs on par or better, confirming the suitability of the developed model and inference procedure. Numéro de notice : 17685 Affiliation des auteurs : non IGN Autre URL associée : vers ResearchGate Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Geodäsie und Geoinformatik : Hanovre : 2020 DOI : 10.13140/RG.2.2.19618.86728 En ligne : https://dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-857.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98165 Robust pose estimation and calibration of catadioptric cameras with spherical mirrors / Sagi Filin in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 1 (January 2020)
[article]
Titre : Robust pose estimation and calibration of catadioptric cameras with spherical mirrors Type de document : Article/Communication Auteurs : Sagi Filin, Auteur ; Grigory Ilizirov, Auteur ; Bashar Elnashef, Auteur Année de publication : 2020 Article en page(s) : pp 33 - 44 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] estimation de pose
[Termes IGN] étalonnage de chambre métrique
[Termes IGN] flux lumineux
[Termes IGN] lentille
[Termes IGN] méthode robuste
[Termes IGN] miroir
[Termes IGN] reconstruction 3D
[Termes IGN] sphère
[Termes IGN] trilatérationRésumé : (Auteur) Catadioptric cameras broaden the field of view and reveal otherwise occluded object parts. They differ geometrically from central-perspective cameras because of light reflection from the mirror surface. To handle these effects, we present new pose-estimation and reconstruction models for imaging through spherical mirrors. We derive a closed-form equivalent to the collinearity principle via which three methods are established to estimate the system parameters: a resection-based one, a trilateration-based one that introduces novel constraints that enhance accuracy, and a direct and linear transform-based one. The estimated system parameters exhibit improved accuracy compared to the state of the art, and analysis shows intrinsic robustness to the presence of a high fraction of outliers. We then show that 3D point reconstruction can be performed at accurate levels. Thus, we provide an in-depth look into the geometrical modeling of spherical catadioptric systems and practical enhancements of accuracies and requirements to reach them. Numéro de notice : A2020-050 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.1.33 Date de publication en ligne : 01/01/2020 En ligne : https://doi.org/10.14358/PERS.86.1.33 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94535
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 1 (January 2020) . - pp 33 - 44[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2020011 SL Revue Centre de documentation Revues en salle Disponible Simulation and analysis of photogrammetric UAV image blocks: influence of camera calibration error / Yilin Zhou in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W5 (May 2019)
[article]
Titre : Simulation and analysis of photogrammetric UAV image blocks: influence of camera calibration error Type de document : Article/Communication Auteurs : Yilin Zhou , Auteur ; Ewelina Rupnik , Auteur ; Christophe Meynard , Auteur ; Christian Thom , Auteur ; Marc Pierrot-Deseilligny , Auteur Année de publication : 2019 Projets : 1-Pas de projet / Conférence : ISPRS 2019, Geospatial Week 10/06/2019 14/06/2019 Enschede Pays-Bas ISPRS OA Annals Article en page(s) : pp 195 - 200 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bloc d'images
[Termes IGN] compensation par faisceaux
[Termes IGN] effet thermique
[Termes IGN] erreur systématique
[Termes IGN] estimation de pose
[Termes IGN] étalonnage de chambre métrique
[Termes IGN] étalonnage en vol
[Termes IGN] image aérienne oblique
[Termes IGN] image captée par drone
[Termes IGN] longueur focale
[Termes IGN] photogrammétrie aérienne
[Termes IGN] point d'appui
[Termes IGN] points homologuesRésumé : (auteur) Unmanned aerial vehicles (UAV) are increasingly used for topographic mapping. The camera calibration for UAV image blocks can be performed a priori or during the bundle block adjustment (self-calibration). For an area of interest with flat, corridor configuration, the focal length of camera is highly correlated with the height of camera. Furthermore, systematic errors of camera calibration accumulate on the longer dimension and cause deformation. Therefore, special precautions must be taken when estimating camera calibration parameters. In this paper, a simulated, error-free aerial image block is generated. error is then added on camera calibration and given as initial solution to bundle block adjustment. Depending on the nature of the error and the investigation purpose, camera calibration parameters are either fixed or re-estimated during the bundle block adjustment. The objective is to investigate how certain errors in the camera calibration impact the accuracy of 3D measurement without the influence of other errors. All experiments are carried out with Fraser camera calibration model being employed. When adopting a proper flight configuration, an error on focal length for the initial camera calibration can be corrected almost entirely during bundle block adjustment. For the case where an erroneous focal length is given for pre-calibration and not re-estimated, the presence of oblique images limits the drift on camera height hence gives better camera pose estimation. Other than that, the error on focal length when neglecting its variation during the acquisition (e.g., due to camera temperature increase) is also investigated; a bowl effect is observed when one focal length is given in camera pre-calibration to the whole image block. At last, a local error is added in image space to simulate camera flaws; this type of error is more difficult to be corrected with the Fraser camera model and the accuracy of 3D measurement degrades substantially. Numéro de notice : A2019-591 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-2-W5-195-2019 Date de publication en ligne : 29/05/2019 En ligne : https://doi.org/10.5194/isprs-annals-IV-2-W5-195-2019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94551
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol IV-2/W5 (May 2019) . - pp 195 - 200[article]BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images / Debaditya Acharya in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)
[article]
Titre : BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images Type de document : Article/Communication Auteurs : Debaditya Acharya, Auteur ; Kourosh Khoshelham, Auteur ; Stephan Winter, Auteur Année de publication : 2019 Article en page(s) : pp 245 - 258 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage profond
[Termes IGN] compréhension de l'image
[Termes IGN] estimation de pose
[Termes IGN] image de synthèse
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] positionnement en intérieur
[Termes IGN] réseau neuronal convolutif
[Termes IGN] structure-from-motionRésumé : (Auteur) The ubiquity of cameras built in mobile devices has resulted in a renewed interest in image-based localisation in indoor environments where the global navigation satellite system (GNSS) signals are not available. Existing approaches for indoor localisation using images either require an initial location or need first to perform a 3D reconstruction of the whole environment using structure-from-motion (SfM) methods, which is challenging and time-consuming for large indoor spaces. In this paper, a visual localisation approach is proposed to eliminate the requirement of image-based reconstruction of the indoor environment by using a 3D indoor model. A deep convolutional neural network (DCNN) is fine-tuned using synthetic images obtained from the 3D indoor model to regress the camera pose. Results of the experiments indicate that the proposed approach can be used for indoor localisation in real-time with an accuracy of approximately 2 m. Numéro de notice : A2019-142 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.02.020 Date de publication en ligne : 05/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.02.020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92480
in ISPRS Journal of photogrammetry and remote sensing > vol 150 (April 2019) . - pp 245 - 258[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019041 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019043 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019042 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt BIM-Tracker: A model-based visual tracking approach for indoor localisation using a 3D building model / Debaditya Acharya in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)PermalinkEquivalent constraints for two-view geometry : Pose solution/pure rotation identification and 3D reconstruction / Qi Cai in International journal of computer vision, vol 127 n° 2 (February 2019)PermalinkThe orthographic projection model for pose calibration of long focal images / Laura F. Julià in IPOL Journal, Image Processing On Line, vol 9 (2019)PermalinkPermalinkPermalinkStructure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation / Xin Wang in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkVision-based localization with discriminative features from heterogeneous visual data / Nathan Piasco (2019)PermalinkDepth-based hand pose estimation : Methods, data, and challenges / James Steven Supančič in International journal of computer vision, vol 126 n° 11 (November 2018)Permalink3D urban geovisualization: in situ augmented and mixed reality experiments / Alexandre Devaux in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-4 (October 2018)PermalinkImage-based synthesis for deep 3D human pose estimation / Grégory Rogez in International journal of computer vision, vol 126 n° 9 (September 2018)PermalinkLandmark based localization in urban environment / Xiaozhi Qu in ISPRS Journal of photogrammetry and remote sensing, vol 140 (June 2018)PermalinkSDF-2-SDF registration for real-time 3D reconstruction from RGB-D data / Miroslava Slavcheva in International journal of computer vision, vol 126 n° 6 (June 2018)PermalinkReal-time accurate 3D head tracking and pose estimation with consumer RGB-D cameras / David Joseph Tan in International journal of computer vision, vol 126 n° 2-4 (April 2018)PermalinkA survey on visual-based localization : on the benefit of heterogeneous data / Nathan Piasco in Pattern recognition, vol 74 (February 2018)PermalinkAdéquation algorithme architecture pour la localisation basée image sur système embarqué / David Vandergucht (2018)PermalinkGéo-référencement précis d'acquisition photogrammétrique de « longues » scènes d'intérieur / Truong Giang Nguyen (2018)PermalinkLocalisation par l'image en milieu urbain : application à la réalité augmentée / Antoine Fond (2018)PermalinkMachine learning and pose estimation for autonomous robot grasping with collaborative robots / Victor Talbot (2018)PermalinkPermalinkAutomatic registration of images to untextured geometry using average shading gradients / Tobias Plötz in International journal of computer vision, vol 125 n° 1-3 (December 2017)Permalink