Deep Learning in Visual Computing and Signal Processing

• Published in: Applied Computational Intelligence and Soft Computing Vol. 2017; no. 2017; pp. 4 - 16
• Main Authors: Xie, Danfeng, Bai, Li, Zhang, Lei
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2017; Hindawi Publishing Corporation; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Artificial intelligence; Brain; Computation; Computational linguistics; Cracks; Data mining; Fault diagnosis; Intelligence; Language processing; Machine learning; Machine vision; Moving object recognition; Natural language interfaces; Neural networks; Researchers; Signal processing; Studies; Theory
• ISSN: 1687-9724; 1687-9732; 1687-9732
• DOI: 10.1155/2017/1320780

Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features from input data. Nowadays, researchers have intensively investigated deep learning algorithms for solving challenging problems in many areas such as image classification, speech recognition, signal processing, and natural language processing. In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply deep learning to specific areas such as road crack detection, fault diagnosis, and human activity detection. Besides, this study also discusses the challenges of designing and training deep neural networks.