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10th Colour and Visual Computing Symposium 2020 (CVCS 2020), Gjøvik, Norway, and Virtual, September 16-17, 2020 / Jean-Baptiste Thomas (2020)
Titre : 10th Colour and Visual Computing Symposium 2020 (CVCS 2020), Gjøvik, Norway, and Virtual, September 16-17, 2020 : Proceedings Type de document : Actes de congrès Auteurs : Jean-Baptiste Thomas, Éditeur scientifique Editeur : Gjøvik [Norvège] : Norwegian University of Science and Technology Année de publication : 2020 Conférence : CVCS 2020, Colour and Visual Computing Symposium 16/09/2020 17/09/2020 Gjøvik et en ligne Norvège Open Access Proceedings Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] analyse visuelle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] couleur (variable spectrale)
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] imagerie médicale
[Termes IGN] luminance lumineuse
[Termes IGN] peinture
[Termes IGN] photographieNuméro de notice : 25892 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Actes En ligne : http://ceur-ws.org/Vol-2688/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96006
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 Fast and accurate target detection based on multiscale saliency and active contour model for high-resolution SAR images / Song Tu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
[article]
Titre : Fast and accurate target detection based on multiscale saliency and active contour model for high-resolution SAR images Type de document : Article/Communication Auteurs : Song Tu, Auteur ; Yi Su, Auteur Année de publication : 2016 Article en page(s) : pp 5729 - 5744 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] détection de changement
[Termes IGN] détection de cible
[Termes IGN] détection de contours
[Termes IGN] granularité d'image
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] imagerie médicale
[Termes IGN] précision des donnéesRésumé : (auteur) The active contour model (ACM) is widely used in target detection of optical and medical images, but multiplicative speckle noise largely interferes with its use in synthetic aperture radar (SAR) images. To overcome this difficulty, a region- and edge-based convex ACM with high efficiency is proposed for target detection in small-scale SAR images. Then, a novel detection algorithm, which combines the advantages of a multiscale saliency detection method and the proposed high-efficiency ACM, is presented to address a large-scale and high-resolution SAR image automatically. Target detection experiments in real and simulated SAR images show that the proposed methods outperform classical ACMs and the popular two-parameter constant false alarm rate detector in terms of efficiency and accuracy. Numéro de notice : A2016-861 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2571309 En ligne : https://doi.org/10.1109/TGRS.2016.2571309 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82892
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 10 (October 2016) . - pp 5729 - 5744[article]A photogrammetric technique for acquiring accurate head surfaces of newborn infants for optical tomography under clinical conditions / M. Abreu De Souza in Photogrammetric record, vol 27 n° 139 (September - November 2012)
[article]
Titre : A photogrammetric technique for acquiring accurate head surfaces of newborn infants for optical tomography under clinical conditions Type de document : Article/Communication Auteurs : M. Abreu De Souza, Auteur ; S. Robson, Auteur ; J. Hebden, Auteur Année de publication : 2012 Article en page(s) : pp 253 - 271 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre
[Termes IGN] enfant
[Termes IGN] imagerie médicale
[Termes IGN] photogrammétrie métrologique
[Termes IGN] reconstruction 3D
[Termes IGN] tomographie
[Termes IGN] visualisation 3DRésumé : (Auteur) Optical tomography (OT) generates 3D images that can be used to monitor blood volume and oxygenation in the brains of newborn infants safely and non-invasively at the bedside. OT image reconstruction requires that source optical fibres and detector bundles (optodes) are brought into optical contact with the head and that the scalp surface geometry and optode positions for each individual infant are accurately known. The photogrammetric challenge is to create affordable tools that can be employed on newborn, and often severely ill, infants in an intensive care environment with a minimum level of intrusion. The paper describes the application and development of a system capable of mapping each individual infant head surface in the clinic and coordinating optode positions and orientations during optical tomography scans. The system developed includes a combination of stereo and multi-photo photogrammetry with a pair of digital SLR cameras and a laser dot projector. A bundle adjustment of the sequential stereo-image pairs is used to generate accurate head meshes that are combined with a photogrammetric network of images from a single camera to bring optodes mounted on a deformable helmet structure into a common coordinate system. This data can then be used in the 3D reconstruction of blood flow in the infant head. Numéro de notice : A2012-461 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/j.1477-9730.2012.00686.x Date de publication en ligne : 18/09/2012 En ligne : https://doi.org/10.1111/j.1477-9730.2012.00686.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31907
in Photogrammetric record > vol 27 n° 139 (September - November 2012) . - pp 253 - 271[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 106-2012031 RAB Revue Centre de documentation En réserve L003 Disponible Image matching and surface registration for 3D reconstruction of a scoliotic torso / I. Detchev in Geomatica, vol 65 n° 2 (June 2011)
[article]
Titre : Image matching and surface registration for 3D reconstruction of a scoliotic torso Type de document : Article/Communication Auteurs : I. Detchev, Auteur ; A. Habib, Auteur ; Y. Chang, Auteur Année de publication : 2011 Article en page(s) : pp 175 - 187 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse diachronique
[Termes IGN] appariement d'images
[Termes IGN] étalonnage d'instrument
[Termes IGN] imagerie médicale
[Termes IGN] médecine humaine
[Termes IGN] photogrammétrie médicale
[Termes IGN] précision submillimétrique
[Termes IGN] reconstruction 3D
[Termes IGN] surveillance sanitaireRésumé : (Auteur) The focus of this research is a hierarchical image matching strategy and a multiple surface registration technique for 3D reconstruction of a scoliotic torso. Scoliosis is a deformity of the human spine most commonly occurring in children. After being detected, periodic examinations via x-rays are traditionally used to measure its progression. However, due to the increased risk of cancer, non-invasive and radiation-free scoliosis detection and progression monitoring methodologies are being researched. For example, quantifying the scoliotic deformity through the torso surface is a valid alternative because of its high correlation with the internal spine curvature. This work proposes a low-cost, multi-camera photogrammetric system for semi-automated 3D reconstruction of a torso surface with a sub-millimetre level accuracy. The paper first describes the system design and calibration for optimal accuracy. It then covers the reconstruction and registration procedures giving insights into the hierarchical image matching strategy and the multiple surface registration technique. Final accuracy is evaluated through the goodness of fit between the reconstructed surface and a more accurate set of points measured by a coordinate measuring machine. Numéro de notice : A2011-542 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5623/cig2011-026 En ligne : https://doi.org/10.5623/cig2011-026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31436
in Geomatica > vol 65 n° 2 (June 2011) . - pp 175 - 187[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 035-2011021 RAB Revue Centre de documentation En réserve L003 Disponible Imagerie / Frederic P. Miller (2010)PermalinkPermalinkvol 55 n° 5-6 - March - June 2001 - Medical Imaging and Photogrammetry (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / International society for photogrammetry and remote sensing (1980 -)PermalinkImage analysis applications and computer graphics, Third International Computer Science Conference, ICSC 95 / Roland T. Chin (1995)PermalinkModèles déformables 2-D et 3-D : application à la segmentation d’images médicales / Isaac Cohen (1992)PermalinkDescription et interprétation des images par la morphologie mathématique / Françoise Preteux (1987)PermalinkQuelques nouveaux modèles morphométriques et leur utilisation en biométrie automatisée par ordinateur / Jean-Paul Rigaut (1984)PermalinkCytologie quantitative et morphologie mathématique / Fernand Meyer (1979)Permalink