The Time Analysis of Perception

Qing Xue; Wen Chao Xu; Ying Xiao Mao; Ping Gang Yu; Yong Hong Li

doi:10.4028/www.scientific.net/AMR.532-533.1201

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 532-533The Time Analysis of Perception

The Time Analysis of Perception

Abstract:

The research of perception time is part of perception model, establish the perception based on depict, calculate the response time and duration of the process of perception, analysis the factor, point of direction of the future research.

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Periodical:

Advanced Materials Research (Volumes 532-533)

Pages:

1201-1204

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.532-533.1201

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Online since:

June 2012

Authors:

Qing Xue, Wen Chao Xu, Ying Xiao Mao, Ping Gang Yu, Yong Hong Li

Keywords:

Duration of Perception, Perception, Response Time of Perception

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References

[1] Robert A, Wilson, Frank C, Keil. The MIT Encyclopedia of the Cognitive Sciences[M]. London: MIT Press, (1999).

Google Scholar

[2] K. Fukushima and T. Imagawa: Recognition and segmentation of connected characters in cursive handwritingwith selective attention, in T. Kohonen, K. Miikisara, 0. Simula and J. Kangas (ed. ), ArtificialNeural Networks, Elsevier Science Publishers B.V., North-Holland, Vol. 1, pp.105-110, (1991).

Google Scholar

[3] B. Julesz and J.R. Bergen, Textons, The Fundamental Elements in Preattentive Vision and Perception of Textures, The Bell System Technical Journal, Vol. 62, No. 6, pp.1619-1645, July-August (1983).

DOI: 10.1002/j.1538-7305.1983.tb03502.x

Google Scholar

[4] K. Fujii and A. Matsuoka and T. Morita, Analysis of the Optical illusion by Lateral Inhibition, Medical electoronics and Biotechnology, Jpan Society of Medical Electronics, pp.25-34, (1967).

Google Scholar

[5] Chickering, D., Meek, C., and Heckerman, D., Largesample learning of bayesian networks is nphard, Proceedings of the 19th Annual Conference on Uncertainty in Artificial Intelligence (UAI-03), pp.124-133, (2003).

Google Scholar