Visual and Camera Sensors
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.
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Format: | Electronic Book Chapter |
Language: | English |
Published: |
Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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100 | 1 | |a Park, Kang Ryoung |4 edt | |
700 | 1 | |a Lee, Sangyoun |4 edt | |
700 | 1 | |a Kim, Euntai |4 edt | |
700 | 1 | |a Park, Kang Ryoung |4 oth | |
700 | 1 | |a Lee, Sangyoun |4 oth | |
700 | 1 | |a Kim, Euntai |4 oth | |
245 | 1 | 0 | |a Visual and Camera Sensors |
260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
300 | |a 1 electronic resource (274 p.) | ||
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338 | |a online resource |b cr |2 rdacarrier | ||
506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a 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. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
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653 | |a self-assembly device | ||
653 | |a 3D point clouds | ||
653 | |a accuracy analysis | ||
653 | |a VSLAM-photogrammetric algorithm | ||
653 | |a portable mobile mapping system | ||
653 | |a low-cost device | ||
653 | |a BIM | ||
653 | |a camera calibration | ||
653 | |a DLT | ||
653 | |a PnP | ||
653 | |a weighted DLT | ||
653 | |a uncertainty | ||
653 | |a covariance | ||
653 | |a robustness | ||
653 | |a visual-inertial | ||
653 | |a semi-direct SLAM | ||
653 | |a multi-sensor fusion | ||
653 | |a side-rear-view monitoring system | ||
653 | |a automatic online calibration | ||
653 | |a Hough-space | ||
653 | |a unmanned aerial vehicle | ||
653 | |a autonomous landing | ||
653 | |a deep-learning-based motion deblurring and marker detection | ||
653 | |a network slimming | ||
653 | |a pruning model | ||
653 | |a convolutional neural network | ||
653 | |a convolutional filter | ||
653 | |a classification | ||
653 | |a multimodal human recognition | ||
653 | |a blur image restoration | ||
653 | |a DeblurGAN | ||
653 | |a CNN | ||
653 | |a facial expression recognition system | ||
653 | |a computer vision | ||
653 | |a multi-scale featured local binary pattern | ||
653 | |a unsharp masking | ||
653 | |a machine learning | ||
653 | |a lens distortion | ||
653 | |a DoF-dependent | ||
653 | |a distortion partition | ||
653 | |a vision measurement | ||
653 | |a pathological site classification | ||
653 | |a in vivo endoscopy | ||
653 | |a computer-aided diagnosis | ||
653 | |a artificial intelligence | ||
653 | |a ensemble learning | ||
653 | |a convolutional auto-encoders | ||
653 | |a local image patch | ||
653 | |a point pair feature | ||
653 | |a plank recognition | ||
653 | |a robotic grasping | ||
653 | |a flying object detection | ||
653 | |a drone | ||
653 | |a image processing | ||
653 | |a camera networks | ||
653 | |a open-pit mine slope monitoring | ||
653 | |a optimum deployment | ||
653 | |a close range photogrammetry | ||
653 | |a three-dimensional reconstruction | ||
653 | |a OCD4M | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/4285 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/76836 |7 0 |z DOAB: description of the publication |