Visual and Camera Sensors

• Autore: Park Kang Ryoung
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 electronic resource (274 p.)
• Soggetto topico: Information technology industries
• Soggetto non controllato: self-assembly device; 3D point clouds; accuracy analysis; VSLAM-photogrammetric algorithm; portable mobile mapping system; low-cost device; BIM; camera calibration; DLT; PnP; weighted DLT; uncertainty; covariance; robustness; visual-inertial; semi-direct SLAM; multi-sensor fusion; side-rear-view monitoring system; automatic online calibration; Hough-space; unmanned aerial vehicle; autonomous landing; deep-learning-based motion deblurring and marker detection; network slimming; pruning model; convolutional neural network; convolutional filter; classification; multimodal human recognition; blur image restoration; DeblurGAN; CNN; facial expression recognition system; computer vision; multi-scale featured local binary pattern; unsharp masking; machine learning; lens distortion; DoF-dependent; distortion partition; vision measurement; pathological site classification; in vivo endoscopy; computer-aided diagnosis; artificial intelligence; ensemble learning; convolutional auto-encoders; local image patch; point pair feature; plank recognition; robotic grasping; flying object detection; drone; image processing; camera networks; open-pit mine slope monitoring; optimum deployment; close range photogrammetry; three-dimensional reconstruction; OCD4M
• Persona (resp. second.): LeeSangyoun; KimEuntai; ParkKang Ryoung
• Sommario/riassunto: This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors.
• Titolo autorizzato: Visual and Camera Sensors
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557360203321
• Lo trovi qui: Univ. Federico II