Machine Learning in Sensors and Imaging
Nam, Hyoungsik
Machine Learning in Sensors and Imaging - Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 - 1 electronic resource (302 p.)
Open Access
Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.
Creative Commons
English
books978-3-0365-3754-2 9783036537535 9783036537542
10.3390/books978-3-0365-3754-2 doi
Technology: general issues
History of engineering & technology
star image image denoising reinforcement learning maximum likelihood estimation mixed Poisson-Gaussian likelihood machine learning-based classification non-uniform foundation stochastic analysis vehicle-pavement-foundation interaction forest growing stem volume coniferous plantations variable selection texture feature random forest red-edge band on-shelf availability semi-supervised learning deep learning image classification machine learning explainable artificial intelligence wildfire risk assessment Naïve bayes transmission-line corridors image encryption compressive sensing plaintext related chaotic system convolutional neural network color prior model object detection piston error detection segmented telescope BP artificial neural network modulation transfer function computer vision intelligent vehicles extrinsic camera calibration structure from motion convex optimization temperature estimation BLDC electric machine protection touchscreen capacitive display SNR stylus laser cutting quality monitoring artificial neural network burr formation cut interruption fiber laser semi-supervised fuzzy noisy real-world plankton marine activity recognition wearable sensors imbalanced activities sampling methods path planning Q-learning neural network YOLO algorithm robot arm target reaching obstacle avoidance
Machine Learning in Sensors and Imaging - Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 - 1 electronic resource (302 p.)
Open Access
Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.
Creative Commons
English
books978-3-0365-3754-2 9783036537535 9783036537542
10.3390/books978-3-0365-3754-2 doi
Technology: general issues
History of engineering & technology
star image image denoising reinforcement learning maximum likelihood estimation mixed Poisson-Gaussian likelihood machine learning-based classification non-uniform foundation stochastic analysis vehicle-pavement-foundation interaction forest growing stem volume coniferous plantations variable selection texture feature random forest red-edge band on-shelf availability semi-supervised learning deep learning image classification machine learning explainable artificial intelligence wildfire risk assessment Naïve bayes transmission-line corridors image encryption compressive sensing plaintext related chaotic system convolutional neural network color prior model object detection piston error detection segmented telescope BP artificial neural network modulation transfer function computer vision intelligent vehicles extrinsic camera calibration structure from motion convex optimization temperature estimation BLDC electric machine protection touchscreen capacitive display SNR stylus laser cutting quality monitoring artificial neural network burr formation cut interruption fiber laser semi-supervised fuzzy noisy real-world plankton marine activity recognition wearable sensors imbalanced activities sampling methods path planning Q-learning neural network YOLO algorithm robot arm target reaching obstacle avoidance