| Peer-Reviewed

Advances and Application of Visual Attention Mechanism

Received: 1 June 2017    Accepted: 24 August 2017    Published: 10 October 2017
Views:       Downloads:
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.

Published in International Journal of Data Science and Analysis (Volume 3, Issue 4)
DOI 10.11648/j.ijdsa.20170304.11
Page(s) 24-27
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Visual Attention, Object Extraction, Image Retrieval, Object Detection, Visual Nervous System

References
[1] 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.
[2] Halely Balaban, Roy Luria. The number of objects determines visual working memory capacity allocation for complex items. NeuroImage, 2015, 119: 54-62.
[3] 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.
[4] 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.
[5] 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.
[6] Jifan Zhou, Haihang Zhang, Xiaowei Ding, et al. Object formation in visual working memory: Evidence from object-based attention. Cognition, 2016, 154: 95-101.
[7] 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.
[8] 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.
[9] Yao Juncai, Liu Guizhong. A novel color image compression algorithm using the human visual contrast sensitivity characteristics. Photonic Sensors, 2017, 7(1): 72-81.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] Alcides X. Benicasa, Marcos G. Quiles, Thiago C. Silva, et al. An object-based visual selection framework. Neurocomputing, 2016, 180: 35-54.
[19] Jufeng Zhao, Xiumin Gao, Guang Lin, et al. An optical information processing-based idea for visual attention analysis. Optik, 2016, 127: 3556-3559.
[20] T Nathan Mundhenk, Laurent Itti. Computational modeling and exploration of contour integration for visual saliency. Biological Cybernetics, 2005, 93(3): 188-212.
[21] Tony Lindeberg. A computational theory of visual receptive fields. Biological Cybernetics, 2013, 107(6): 589-635.
[22] Duzhen Zhang, Chuancai Liu. A salient object detection framework beyond top-down and bottom-up mechanism. Biologically Inspired Cognitive Architectures, 2014, 9: 1-8.
[23] Anna Schubö, Hermann J. Müller. Selecting and ignoring salient objects within and across dimensions in visual search. Brain Research, 2009, 1283: 84-101.
Cite This Article
  • APA Style

    Li Gun, Xu Fei, Yu Lei, Zhang Liang. (2017). Advances and Application of Visual Attention Mechanism. International Journal of Data Science and Analysis, 3(4), 24-27. https://doi.org/10.11648/j.ijdsa.20170304.11

    Copy | Download

    ACS Style

    Li Gun; Xu Fei; Yu Lei; Zhang Liang. Advances and Application of Visual Attention Mechanism. Int. J. Data Sci. Anal. 2017, 3(4), 24-27. doi: 10.11648/j.ijdsa.20170304.11

    Copy | Download

    AMA Style

    Li Gun, Xu Fei, Yu Lei, Zhang Liang. Advances and Application of Visual Attention Mechanism. Int J Data Sci Anal. 2017;3(4):24-27. doi: 10.11648/j.ijdsa.20170304.11

    Copy | Download

  • @article{10.11648/j.ijdsa.20170304.11,
      author = {Li Gun and Xu Fei and Yu Lei and Zhang Liang},
      title = {Advances and Application of Visual Attention Mechanism},
      journal = {International Journal of Data Science and Analysis},
      volume = {3},
      number = {4},
      pages = {24-27},
      doi = {10.11648/j.ijdsa.20170304.11},
      url = {https://doi.org/10.11648/j.ijdsa.20170304.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20170304.11},
      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.},
     year = {2017}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Advances and Application of Visual Attention Mechanism
    AU  - Li Gun
    AU  - Xu Fei
    AU  - Yu Lei
    AU  - Zhang Liang
    Y1  - 2017/10/10
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ijdsa.20170304.11
    DO  - 10.11648/j.ijdsa.20170304.11
    T2  - International Journal of Data Science and Analysis
    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
    SP  - 24
    EP  - 27
    PB  - Science Publishing Group
    SN  - 2575-1891
    UR  - https://doi.org/10.11648/j.ijdsa.20170304.11
    AB  - 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.
    VL  - 3
    IS  - 4
    ER  - 

    Copy | Download

Author Information
  • Department of Biomedical Engineering, School of Electronic Information Engineering, Xi’An Technological University, Xi’An, China

  • Department of Biomedical Engineering, School of Electronic Information Engineering, Xi’An Technological University, Xi’An, China

  • Department of Biomedical Engineering, School of Electronic Information Engineering, Xi’An Technological University, Xi’An, China

  • Department of Biomedical Engineering, School of Electronic Information Engineering, Xi’An Technological University, Xi’An, China

  • Sections