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Deep Learning for Computer Vision: A Brief Review
Joint Authors
Protopapadakis, Eftychios
Voulodimos, Athanasios
Doulamis, Nikolaos
Doulamis, Anastasios
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-02-01
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases.
This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders.
A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation.
Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.
American Psychological Association (APA)
Voulodimos, Athanasios& Doulamis, Nikolaos& Doulamis, Anastasios& Protopapadakis, Eftychios. 2018. Deep Learning for Computer Vision: A Brief Review. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1130824
Modern Language Association (MLA)
Voulodimos, Athanasios…[et al.]. Deep Learning for Computer Vision: A Brief Review. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1130824
American Medical Association (AMA)
Voulodimos, Athanasios& Doulamis, Nikolaos& Doulamis, Anastasios& Protopapadakis, Eftychios. Deep Learning for Computer Vision: A Brief Review. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1130824
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130824