期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

视觉选择性注意计算模型 被引量：4

Computational Model of Visual Selective Attention
在线阅读 下载PDF
导出
摘要 提出一种用于智能机器人的视觉注意计算模型.受生物学启发,该模型模仿人类自下而上和自上而下两种视觉选择性注意过程.通过提取输入图像的多尺度下的多个底层特征,在频域分析各特征图的幅度谱,在空域构造相应的特征显著图.根据显著图,计算出注意焦点的位置和注意区域的大小,结合给定的任务在各注意焦点之间进行视觉转移.在多幅自然图像上进行实验,并给出相应的实验结果、定性和定量分析.实验结果与人类视觉注意结果一致,表明该计算模型在注意效果、运算速度等方面有效. A new computational model of visual attention for intelligent robots is proposed. Motivated by biology, this model simulates both the bottom-up and top-down human visual selective attention mechanisms. By extracting multiple lowlevel features of the input image with multiple scales and analyzing the amplitude spectra of these feature maps in frequency domain, the corresponding saliency map is constructed in spatial domain. Based on the saliency map, the position and size of potential focuses of attention are computed. According to the given target, attention is changed among the focuses of attention. The model is tested on many natural images. Experiment results and qualitative and quantitative analysis are presented. The proposed results are consistent with human visual attention results. It is indicated that this model is effective in attention effect and computational speed.
作者 张巧荣 顾国昌 肖会敏
机构地区 哈尔滨工程大学计算机科学与技术学院 河南财经学院计算机与信息工程学院 河南财经学院数学与信息科学系
出处 《机器人》 EI CSCD 北大核心 2009年第6期574-580,共7页 Robot
基金 国家自然科学基金资助项目(60774041)
关键词 视觉注意 显著图 显著区域 注意焦点 visual attention saliency map salient region focus of attention
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献8

  • 1Itti L, Koch C. Computational modelling of visual attention[J]. Nature Reviews Neuroscience, 2001, 2(3): 194-230.
  • 2Itti L, Koch C. Feature combination strategies for saliencybased visual attention systems[J]. Journal of Electronic Imaging, 2001, 10(1): 161-169.
  • 3Navalpakkam V, Itti L. An integrated model of top-down and bottom-up attention for optimizing detection speed[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA, USA: IEEE Computer Society, 2006: 2049-2056.
  • 4Ma Y F, Zhang H J. Contrast-based image attention analysis by using fuzzy growing[C]//ACM International Conference on Multimedia. New York, USA: ACM, 2003: 374-381.
  • 5Sun Y R, Fisher R. Object-based visual attention for computer vision[J]. Artificial Intelligence, 2003, 146(1): 77-123.
  • 6张鹏,王润生.基于视点转移和视区追踪的图像显著区域检测[J].软件学报,2004,15(6):891-898. 被引量：53
  • 7Won W J, Ban S W, Lee M. Real time implementation of a selective attention model for the intelligent robot with autonomous mental development[C]//IEEE International Symposium on Industrial Electronics. Piscataway, NJ, USA: IEEE Industrial Electronics Society, 2005 : 1309-1314.
  • 8Hou X D, Zhang L Q. Saliency detection: A spectral residual approach[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA, USA: IEEE Computer Society, 2007: 1-8.

二级参考文献13

  • 1Bourque E, Dudek G, Ciaravola P. Robotic sightseeing: A method for automatically creating virtual environments. In: Giralt G, ed.Proc. of the IEEE Conf. on Robotics and Automation. Leuven: IEEE Press, 1998. 3186～3191.
  • 2Kadir T, Brady M. Saliency, scale and image description. International Journal of Computer Vision, 2001,45(2):83-105.
  • 3Gesu VD, Valenti C, Strinati L. Local operators to detect regions of interest. Pattern Recognition Letters, 1997,18(11-13):1077-1081.
  • 4Wai WYK, Tsotsos JK. Directing attention to onset and offset of image events for eye-head movement control. In: Huang T, ed.Proc. of the Int'l Association for Pattern Recognition Workshop on Visual Behaviors. Seattle: IEEE Press, 1994. 79～84.
  • 5Stentiford FWM. An evolutionary programming approach to the simulation of visual attention. In: Kim JH, ed. Proc. of the IEEE Congress on Evolutionary Computation. Seoul: IEEE Press, 2001. 851-858.
  • 6Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1998,20(11):1254-1259.
  • 7Itti L, Koch C. Computational modeling of visual attention. Nature Reviews Neuroscience, 2001,2(3):194-230.
  • 8Itti L, Koch C. Feature combination strategies for saliency-based visual attention systems. Journal of Electronic Imaging,2001,10(1):161-169.
  • 9Yee H, Pattanaik SN, Greenberg DP. Spatiotemporal sensitivity and visual attention for efficient rendering of dynamic environments. ACM Trans. on Computer Graphics, 2001,20(1):39-65.
  • 10Boccignone G, Ferraro M, Caelli T. Generalized spatio-chromatic diffusion. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002,24(10): 1298-1309.

共引文献52

同被引文献78

  • 1王波.基于多尺度小波的棉花异纤检测算法[J].控制工程,2009,16(S1):173-175. 被引量：7
  • 2田媚,罗四维,齐英剑,廖灵芝.基于视觉系统“What”和“Where”通路的图像显著区域检测[J].模式识别与人工智能,2006,19(2):155-160. 被引量：5
  • 3王明佳,张旭光,王延杰.利用广义霍夫变换对相交目标进行轨迹预测[J].光学技术,2006,32(3):428-430. 被引量：2
  • 4Khanh V, Hua K A, and Tavanapong W. Image retrieval based on regions of interest [J].IEEE Transactions on Knowledge and Data Engineering,2003,15(4): 1045- 1049.
  • 5Bulthoff H H, Lee S W, and Poggio T, et al. Biologically Motivated Computer Vision[M].New York:Springer Publishing Company,2003.
  • 6Itti L, Korch C, and Niebur E. A model of saliency-based visual attention for rapid scene analysis [J].IEEE Transactions on Pattern Analysis and Machine Intellingence,1998,11 (20)!1254- 1259.
  • 7Itti L, Korch C. Computational modeling of visual attention[J]. Nature Reviews Neuroscience,2001,2(3):194-230.
  • 8Kastner S, Ungerleider L G. Mechanisms of visual attention in the human cortex[J].Annu ,Trv,Neurosci,2000 ( 23 ) :315 -341.
  • 9Hou X D, Zhang L Q. Saliency detection: A spectral residual approach [C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fos Alamitos, CA, USA:IEEE Computer Society,2007.
  • 10Itti L, Korch C.Feature combination strategies for saliency- based visual attention systems[J].Journal of Electronic Imaging, 2001,10(1):161-169.

引证文献4

二级引证文献8

机器人
2009年 第6期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部