Advances and Application of Visual Attention Mechanism
International Journal of Data Science and Analysis
Volume 3, Issue 4, August 2017, Pages: 24-27
Received: Jun. 1, 2017; Accepted: Aug. 24, 2017; Published: Oct. 10, 2017
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Authors
Li Gun, Department of Biomedical Engineering, School of Electronic Information Engineering, Xi’An Technological University, Xi’An, China
Xu Fei, Department of Biomedical Engineering, School of Electronic Information Engineering, Xi’An Technological University, Xi’An, China
Yu Lei, Department of Biomedical Engineering, School of Electronic Information Engineering, Xi’An Technological University, Xi’An, China
Zhang Liang, Department of Biomedical Engineering, School of Electronic Information Engineering, Xi’An Technological University, Xi’An, China
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Abstract
Senses and cognition of humans are mainly done by the visual nervous system. Most of the information people absorb from the world all conducted by the visual system. Therefore, visual attention mechanism is very important for exploring the visual system. In this paper, several basic problems of visualization of cellular electrical activity and visual information processing in the central nervous system are reviewed; then, models of visual attention mechanism are systematically reviewed. Finally, application of the visual attention mechanism in medical image segmentation is discussed.
Keywords
Visual Attention, Object Extraction, Image Retrieval, Object Detection, Visual Nervous System
To cite this article
Li Gun, Xu Fei, Yu Lei, Zhang Liang, Advances and Application of Visual Attention Mechanism, International Journal of Data Science and Analysis. Vol. 3, No. 4, 2017, pp. 24-27. doi: 10.11648/j.ijdsa.20170304.11
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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