Computer Vision and Machine Learning for Intelligent Sensing Systems

doi:10.3390/books978-3-0365-7869-9

Download Url(s)

https://mdpi.com/books/pdfview/book/7500

Contributor(s)

Tian, Jing (editor)

Language

English

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Abstract

The reprint offers a selection of high-quality research articles that tackle the major difficulties in computer vision and machine learning for intelligent sensing systems from both theoretical and practical standpoints. This publication includes intelligent sensing techniques, twelve foundational investigations into sense-making methods, and discusses particular uses of intelligent sensing systems in autonomous driving and virtual reality.

URI

https://directory.doabooks.org/handle/20.500.12854/101403

Keywords

mobile edge computaing; simultaneous wireless information and power transfer; energy minimization; 5G; wireless sensing network; IoT; fiber bragg grating; optical fiber sensor; distributed temperature sensor; deep learning algorithms; fully connected neural network; convolutional neural network; MADS dataset; human segmentation; human tracking; convolutional neural networks; targeted advertising; emotion-based recommendation; augmented reality; computer vision; deep learning; clustering; similarity measure; geodesic measure; Euclidean measure; depth fusion; TSDF; sensor noises; gaze estimation based on feature; eye landmark detection; self-attention; synthetic eye images; heuristic attention; perceptual grouping; self-supervised learning; visual representation learning; intelligent sensors; robotics; event-based camera; contrast maximization; optical flow; motion estimation; human action recognition; graph neural network; attention module; big five personality traits; cultural algorithm; hyper-parameter optimization; personality perception; online self-calibration; voxel information; n/a