Visual Sensors

• Päätekijä: Reinoso Garcia, Oscar (auth)
• Muut tekijät: Payá, Luis (auth)
• Aineistotyyppi: Elektroninen Kirjan osa
• Kieli: englanti
• Julkaistu: MDPI - Multidisciplinary Digital Publishing Institute 2020
• Aiheet: History of engineering & technology; recognition algorithm; n/a; 3D ConvNets; consistent line clustering; skeletal data; fused point and line feature matching; soft decision tree; texture retrieval; vision system; laser sensor; neural network; iris segmentation; correlation filters; embedded systems; underwater imaging; stereo vision; seam-line; image processing; quality control; dynamic programming; visual information fusion; semantic segmentation; parallel line; textile retrieval; structure extraction; line scan camera; orientation relevance; measurement error; rotation-angle; star image prediction; convolutional neural network (CNN); tightly-coupled VIO; visual sensors; stereo; parking assist system; visual detection; omnidirectional imaging; RGB-D SLAM; narrow butt joint; appearance-temporal features; vision-guided robotic grasping; scale invariance; support vector machine (SVM); straight wing aircraft; statistical information of gray-levels differences; Local Binary Patterns; robotics; mobile robots; textile localization; indoor environment; CLOSIB; geometric moments; perceptually uniform histogram; single-shot 3D shape measurement; salient region detection; person re-identification; calibration; stereo camera; simplified initialization strategy; LSTM; SLAM; image mosaic; convolutional neural network; lane marking detection; finger alphabet; robot manipulation; patrol robot; inverse compositional Gauss-Newton algorithm; checkerboard; action localization; hybrid histogram descriptor; pivotal frames; lane marking reconstruction; warp function; visual localization; RGB-D; automatic calibration; Siamese network; object recognition; human visual system; LRF; Gray code; visual tracking; motion-aware; visual odometry; adaptive update strategy; Manhattan frame estimation; vibration; confidence response map; lane marking; 3D reconstruction; indoor visual SLAM; pose estimation; global feature descriptor; sweet pepper; texture classification; ego-motion estimation; pose estimates; planes intersection; adaptive model; support vector machines; motif co-occurrence histogram; handshape recognition; non-rigid reconstruction; camera calibration; map representation; optical flow; robotic welding; FOV; background dictionary; appearance based model; Visual Sensors; spatial transformation; star sensor; image retrieval; depth vision; iterative closest point; automated design; semantic mapping; regression based model; seam tracking; image binarization; GTAW; boosted decision tree; pedestrian detection; presentation attack detection; visible light and near-infrared light camera sensors; large field of view; fringe projection profilometry; sensors combination; catadioptric sensor; RGB-D sensor; texture description; UAV image; motion estimation; extrinsic calibration; visual sensor; advanced driver assistance system (ADAS); content-based image retrieval; action segmentation; stereo-vision; visual mapping; around view monitor (AVM) system; illumination; speed measurement; Richardson-Lucy algorithm; digital image correlation; point cloud; receptive field correspondence; human visual attention; camera pose; sign language; symmetry axis; end-to-end architecture; local parallel cross pattern; iris recognition; depth image registration
• Linkit: DOAB: download the publication; DOAB: description of the publication

Visual sensors are able to capture a large quantity of information from the environment around them. A wide variety of visual systems can be found, from the classical monocular systems to omnidirectional, RGB-D, and more sophisticated 3D systems. Every configuration presents some specific characteri...