Computer Vision and Machine Learning for Intelligent Sensing Systems

• Other Authors: Tian, Jing (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Information technology industries; Computer science; 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
• Online Access: DOAB: download the publication; DOAB: description of the publication

Summary

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.

Physical Description

1 electronic resource (244 p.)

ISBN

books978-3-0365-7869-9
9783036578682
9783036578699

Access

Open Access