Visual Sensors

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...

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Autors principals:	Reinoso Garcia, Oscar, Payá, Luis
Format:	Online
Idioma:	anglès
Publicat:	MDPI - Multidisciplinary Digital Publishing Institute 2021
Matèries:	TA1-2040 T1-995 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
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Sumari:	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 characteristics that make them useful for solving different problems. Their range of applications is wide and varied, including robotics, industry, agriculture, quality control, visual inspection, surveillance, autonomous driving, and navigation aid systems. In this book, several problems that employ visual sensors are presented. Among them, we highlight visual SLAM, image retrieval, manipulation, calibration, object recognition, navigation, etc.

Visual Sensors

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