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Computational Intelligence and Neuroscience
Volume 2018, Article ID 7068349, 13 pages
Review Article

Deep Learning for Computer Vision: A Brief Review

1Department of Informatics, Technological Educational Institute of Athens, 12210 Athens, Greece
2National Technical University of Athens, 15780 Athens, Greece

Correspondence should be addressed to Athanasios Voulodimos; rg.autn.liam@vsonaht

Received 17 June 2017; Accepted 27 November 2017; Published 1 February 2018

Academic Editor: Diego Andina

Copyright © 2018 Athanasios Voulodimos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.