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Intelligent Imaging and Analysis / DaeEun Kim (2020)  

Public 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 ordinateur Résumé : (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 spines Numéro 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