TY  - GEN
AU  - Reinoso Garcia,Oscar
AU  - Payá,Luis
TI  - Visual Sensors
SN  - books978-3-03928-339-2
PY  - 2020///
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - recognition algorithm
KW  - n/a
KW  - 3D ConvNets
KW  - consistent line clustering
KW  - skeletal data
KW  - fused point and line feature matching
KW  - soft decision tree
KW  - texture retrieval
KW  - vision system
KW  - laser sensor
KW  - neural network
KW  - iris segmentation
KW  - correlation filters
KW  - embedded systems
KW  - underwater imaging
KW  - stereo vision
KW  - seam-line
KW  - image processing
KW  - quality control
KW  - dynamic programming
KW  - visual information fusion
KW  - semantic segmentation
KW  - parallel line
KW  - textile retrieval
KW  - structure extraction
KW  - line scan camera
KW  - orientation relevance
KW  - measurement error
KW  - rotation-angle
KW  - star image prediction
KW  - convolutional neural network (CNN)
KW  - tightly-coupled VIO
KW  - visual sensors
KW  - stereo
KW  - parking assist system
KW  - visual detection
KW  - omnidirectional imaging
KW  - RGB-D SLAM
KW  - narrow butt joint
KW  - appearance-temporal features
KW  - vision-guided robotic grasping
KW  - scale invariance
KW  - support vector machine (SVM)
KW  - straight wing aircraft
KW  - statistical information of gray-levels differences
KW  - Local Binary Patterns
KW  - robotics
KW  - mobile robots
KW  - textile localization
KW  - indoor environment
KW  - CLOSIB
KW  - geometric moments
KW  - perceptually uniform histogram
KW  - single-shot 3D shape measurement
KW  - salient region detection
KW  - person re-identification
KW  - calibration
KW  - stereo camera
KW  - simplified initialization strategy
KW  - LSTM
KW  - SLAM
KW  - image mosaic
KW  - convolutional neural network
KW  - lane marking detection
KW  - finger alphabet
KW  - robot manipulation
KW  - patrol robot
KW  - inverse compositional Gauss-Newton algorithm
KW  - checkerboard
KW  - action localization
KW  - hybrid histogram descriptor
KW  - pivotal frames
KW  - lane marking reconstruction
KW  - warp function
KW  - visual localization
KW  - RGB-D
KW  - automatic calibration
KW  - Siamese network
KW  - object recognition
KW  - human visual system
KW  - LRF
KW  - Gray code
KW  - visual tracking
KW  - motion-aware
KW  - visual odometry
KW  - adaptive update strategy
KW  - Manhattan frame estimation
KW  - vibration
KW  - confidence response map
KW  - lane marking
KW  - 3D reconstruction
KW  - indoor visual SLAM
KW  - pose estimation
KW  - global feature descriptor
KW  - sweet pepper
KW  - texture classification
KW  - ego-motion estimation
KW  - pose estimates
KW  - planes intersection
KW  - adaptive model
KW  - support vector machines
KW  - motif co-occurrence histogram
KW  - handshape recognition
KW  - non-rigid reconstruction
KW  - camera calibration
KW  - map representation
KW  - optical flow
KW  - robotic welding
KW  - FOV
KW  - background dictionary
KW  - appearance based model
KW  - Visual Sensors
KW  - spatial transformation
KW  - star sensor
KW  - image retrieval
KW  - depth vision
KW  - iterative closest point
KW  - automated design
KW  - semantic mapping
KW  - regression based model
KW  - seam tracking
KW  - image binarization
KW  - GTAW
KW  - boosted decision tree
KW  - pedestrian detection
KW  - presentation attack detection
KW  - visible light and near-infrared light camera sensors
KW  - large field of view
KW  - fringe projection profilometry
KW  - sensors combination
KW  - catadioptric sensor
KW  - RGB-D sensor
KW  - texture description
KW  - UAV image
KW  - motion estimation
KW  - extrinsic calibration
KW  - visual sensor
KW  - advanced driver assistance system (ADAS)
KW  - content-based image retrieval
KW  - action segmentation
KW  - stereo-vision
KW  - visual mapping
KW  - around view monitor (AVM) system
KW  - illumination
KW  - speed measurement
KW  - Richardson-Lucy algorithm
KW  - digital image correlation
KW  - point cloud
KW  - receptive field correspondence
KW  - human visual attention
KW  - camera pose
KW  - sign language
KW  - symmetry axis
KW  - end-to-end architecture
KW  - local parallel cross pattern
KW  - iris recognition
KW  - depth image registration
N1  - Open Access
N2  - 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
UR  - https://mdpi.com/books/pdfview/book/2141
UR  - https://directory.doabooks.org/handle/20.500.12854/62289
ER  -