Machine Learning in Sensors and Imaging
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, mach...
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (302 p.) |
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| 245 | 1 | 0 | |a Machine Learning in Sensors and Imaging |
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| 520 | |a 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. | ||
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| 650 | 7 | |a History of engineering & technology |2 bicssc | |
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| 653 | |a reinforcement learning | ||
| 653 | |a maximum likelihood estimation | ||
| 653 | |a mixed Poisson–Gaussian likelihood | ||
| 653 | |a machine learning-based classification | ||
| 653 | |a non-uniform foundation | ||
| 653 | |a stochastic analysis | ||
| 653 | |a vehicle–pavement–foundation interaction | ||
| 653 | |a forest growing stem volume | ||
| 653 | |a coniferous plantations | ||
| 653 | |a variable selection | ||
| 653 | |a texture feature | ||
| 653 | |a random forest | ||
| 653 | |a red-edge band | ||
| 653 | |a on-shelf availability | ||
| 653 | |a semi-supervised learning | ||
| 653 | |a deep learning | ||
| 653 | |a image classification | ||
| 653 | |a machine learning | ||
| 653 | |a explainable artificial intelligence | ||
| 653 | |a wildfire | ||
| 653 | |a risk assessment | ||
| 653 | |a Naïve bayes | ||
| 653 | |a transmission-line corridors | ||
| 653 | |a image encryption | ||
| 653 | |a compressive sensing | ||
| 653 | |a plaintext related | ||
| 653 | |a chaotic system | ||
| 653 | |a convolutional neural network | ||
| 653 | |a color prior model | ||
| 653 | |a object detection | ||
| 653 | |a piston error detection | ||
| 653 | |a segmented telescope | ||
| 653 | |a BP artificial neural network | ||
| 653 | |a modulation transfer function | ||
| 653 | |a computer vision | ||
| 653 | |a intelligent vehicles | ||
| 653 | |a extrinsic camera calibration | ||
| 653 | |a structure from motion | ||
| 653 | |a convex optimization | ||
| 653 | |a temperature estimation | ||
| 653 | |a BLDC | ||
| 653 | |a electric machine protection | ||
| 653 | |a touchscreen | ||
| 653 | |a capacitive | ||
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| 653 | |a SNR | ||
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| 653 | |a activity recognition | ||
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| 653 | |a Q-learning | ||
| 653 | |a neural network | ||
| 653 | |a YOLO algorithm | ||
| 653 | |a robot arm | ||
| 653 | |a target reaching | ||
| 653 | |a obstacle avoidance | ||
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