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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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
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.
Advances and Application of Visual Attention Mechanism, International Journal of Data Science and Analysis.
Vol. 3, No. 4,
2017, pp. 24-27.
Francesca C. Fortenbaugh, Lynn C. Robertson, Michael Esterman. Changes in the distribution of sustained attention alter the perceived structure of visual space. Vision Research, 2017, 131: 26-36.
Halely Balaban, Roy Luria. The number of objects determines visual working memory capacity allocation for complex items. NeuroImage, 2015, 119: 54-62.
Helen F. Dodd, Julia Vogt, Nilgun Turkileri, et al. Task relevance of emotional information affects anxiety-linked attention bias in visual search, Biological Psychology, 2017, 122: 13-20.
Sven-Thomas Graupner, Sebastian Pannasch, Boris M. Velichkovsky. Saccadic context indicates information processing within visual fixations: Evidence from event-related potentials and eye-movements analysis of the distractor effect, International Journal of Psychophysiology, 2011, 80: 54-62.
Kenji Fujii, Shinofu Sugi, Yoichi Ando. Textural properties corresponding to visual perception based on the correlation mechanism in the visual system. Psychological Research, 2003, 67: 197-208.
Jifan Zhou, Haihang Zhang, Xiaowei Ding, et al. Object formation in visual working memory: Evidence from object-based attention. Cognition, 2016, 154: 95-101.
Wanyi Li, Peng Wang, Hong Qiao. Top-down visual attention integrated particle filter for robust object tracking. Signal Processing: Image Communication, 2016, 43: 28-41.
Yayun Ren, Benlian Xu, Peiyi Zhu, Mingli Lu, et al. A multiCell visual tracking algorithm using multi-task particle swarm optimization for low-contrast image sequences. Applied Intelligence, 2016, 45(4): 1129-1147.
Yao Juncai, Liu Guizhong. A novel color image compression algorithm using the human visual contrast sensitivity characteristics. Photonic Sensors, 2017, 7(1): 72-81.
Huang Chaobing, Liu Quan, Yu Shengsheng. Regions of interest extraction from color image based on visual saliency. The Journal of Supercomputing, 2011, 58(1): 20-33.
Qiang Zhou, Limin Ma, Mehmet Celenk, et al. Content-based image retrieval based on ROI detection and relevance feedback. Multimedia Tools and Applications, 2005, 27(2): 251-281.
Feng Jing, Ma Long, Bi Fukun, et al. A coarse-to-fine image registration method based on visual attention model. Science China Information Sciences, 2014, 57(12): 1-10.
DH Hubel, TN Wiesel. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Journal of Physiology, 1962, 160(1): 106-154.
Corrado Corradi-Dell’Acqua, Gereon R. Fink, Ralph Weidner. Selecting category specific visual information: Top-down and bottom-up control of object based attention. Consciousness and Cognition, 2015, 35: 330-341.
Yuhua Zheng, Yan Meng, Yaochu Jin. Object recognition using a bio-inspired neuron model with bottom-up and top-down pathways. Neurocomputing, 2011, 74: 3158-3169.
Roman Borisyuk, Yakov Kazanovich, David Chik, et al. A neural model of selective attention and object segmentation in the visual scene: An approach based on partial synchronization and star-like architecture of connections. Neural Networks, 2009, 22: 707-719.
Quoc Do, Lakhmi Jain. Application of neural processing paradigm in visual landmark recognition and autonomous robot navigation. Neural Computing and Applications, 2010, 19(2): 237-254.
Alcides X. Benicasa, Marcos G. Quiles, Thiago C. Silva, et al. An object-based visual selection framework. Neurocomputing, 2016, 180: 35-54.
Jufeng Zhao, Xiumin Gao, Guang Lin, et al. An optical information processing-based idea for visual attention analysis. Optik, 2016, 127: 3556-3559.
T Nathan Mundhenk, Laurent Itti. Computational modeling and exploration of contour integration for visual saliency. Biological Cybernetics, 2005, 93(3): 188-212.
Tony Lindeberg. A computational theory of visual receptive fields. Biological Cybernetics, 2013, 107(6): 589-635.
Duzhen Zhang, Chuancai Liu. A salient object detection framework beyond top-down and bottom-up mechanism. Biologically Inspired Cognitive Architectures, 2014, 9: 1-8.
Anna Schubö, Hermann J. Müller. Selecting and ignoring salient objects within and across dimensions in visual search. Brain Research, 2009, 1283: 84-101.