Advances in Deep-Learning-Based Sensing, Imaging, and Video Processing

Format:

Book

Author/Creator:

Zhang, Yun, Editor.

Contributor:

Kwong, Sam, Editor.

Long, Xu, Editor.

Zhao, Tiesong, Editor.

Zhang, Yun

Kwong, Sam

Long, Xu

Zhao, Tiesong

Language:

English

Physical Description:

1 online resource

Place of Publication:

Basel MDPI - Multidisciplinary Digital Publishing Institute 2024

Language Note:

English

Summary:

This Topical Collection focuses on fundamental and applied research on deep learning based visual sensing, imaging and video processing. Deep learning techniques have shown their capabilities to discover knowledge from massive unstructured data, providing data-driven solutions for representation and decision making. They have demonstrated significant technical advancement potential for many research fields and applications, such as sensors and imaging, audio-visual signal processing, and pattern recognition. Today, with the rapid advancements of advanced deep learning models, such as convolutional neural network (CNN), deep neural network (DNN), recurrent neural network (RNN), generative adversarial network (GAN), and transformer network, learning techniques, such as transfer learning, reinforcement learning, federal learning, multi-task learning, and meta-learning, and the increasing demands around effective visual signal processing, new opportunities are emerging in deep-learning-based sensing, imaging, and video processing.