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- Athanasios Voulodimos
- Department of Informatics, Technological Educational Institute of Athens, 12210 Athens, Greece
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- Nikolaos Doulamis
- National Technical University of Athens, 15780 Athens, Greece
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- Anastasios Doulamis
- National Technical University of Athens, 15780 Athens, Greece
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- Eftychios Protopapadakis
- National Technical University of Athens, 15780 Athens, Greece
説明
<jats:p>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.</jats:p>
収録刊行物
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- Computational Intelligence and Neuroscience
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Computational Intelligence and Neuroscience 2018 1-13, 2018
Hindawi Limited