Visual attention analysis by pseudo gravitational field

MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums

Volumn , Issue , 2009, Pages 553-556

  Yang, Yezhou ^a   Song, Mingli ^a   Li, Na ^a   Bu, Jiajun ^a   Chen, Chun ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Attention analysis; Gravitational field; Simulation algorithm

Indexed keywords

ANALYSIS METHOD; APPLICATION AREA; DOWN SAMPLING; FIXATION POINT; GRAVITATIONAL FIELDS; ITERATION ALGORITHMS; VISUAL ATTENTION; VISUAL COGNITION;

ALGORITHMS; GRAVITATIONAL EFFECTS; METHOD OF MOMENTS; MULTIMEDIA SYSTEMS; TECHNICAL PRESENTATIONS;

PIXELS;

EID: 72449139571     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1631272.1631354     Document Type: Conference Paper

References (12)

1. C.Koch and S.Ullman. Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology, 4:219-227, 1985.
2. E.Niebur and C.Koch. Computational architectures for attention. R. Parasuraman, (Ed.), The attentive brain, Cambridge, MA: MIT Press.
3. J.M.Wolfe and K. Cave. Deploying visual attention: The guided search model. John Wiley & Sons Ltd., 1990.
4. L.Itti, C.Koch, and E.Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20(11):1254-1259, 1998.
5. H. Liu, S. Jiang, Q. Huang, and et al. Region-based visual attention analysis with its application in image browsing on small displays. In Proceedings of the 15th international conference on Multimedia, 2007.
6. S. Lloyd and Y. Ng. Black hole computers. Scientific American, 291:52-61, 2004.
7. Y. Ma and H. Zhang. Contrast-based image attention analysis by using fuzzy growing. ACM Multimedia, 34:374-381, 2003.
8. O. L. Meur and P. L. Callet. A coherent computational approach to model bottom-up visual attention. IEEE Trans. on Pattern Analysis and Machine Intelligence, 28(5):802-817, 2006.
9. R.Milanese, S.Gil, and T.Pun. attentive mechanism for dynamic and static scene analysis. Optical Engineering, 34:2428-2434, 1995.
10. S.Baluja and D.A.Pomerleau. Expectation-based selective attention dor visual monitoring and control of a robot vehicle. Robotics and Autonomous System, 22(3-4):329-344, Dec 1997.
11. J. K. Tsotsos, S. M. Culhane, W. Wai, and et al. Modeling visual attention via selective tuning. Artificial Intelligence, 78:507-545, 1995.
12. W.James. The Principles of Psychology. Holt, New York, 1890.