Object Detection Based on Visual Selective Attention Mechanism

Jian Zhong Sun; En Hai Liu; Cui Bin Li

doi:10.4028/www.scientific.net/AMR.178.350

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 178Object Detection Based on Visual Selective...

Object Detection Based on Visual Selective Attention Mechanism

Abstract:

Visual selective attention modeling is an advanced topic in cognitive information perception. According to human visual perception theory, which is based on the introduction of the representative visual attention model (Itti model), in its initial stages of feature extraction, in order to form a new guidance note of the saliency map, new low-level visual features are introduced, which achieves a more accurate target detection. The results prove that this method can avoid the leakage detection to some extent, which makes attention regional more close to the actuality of biological visual system.

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Periodical:

Advanced Materials Research (Volume 178)

Pages:

350-354

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.178.350

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Online since:

December 2010

Authors:

Jian Zhong Sun, En Hai Liu, Cui Bin Li

Keywords:

Saliency Map, Target Detection, Visual Selective Attention

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References

[1] Christof-Koch: Selective Visual Attention and Computational Models. CNSBi. Read by researchers in: 100% Biological Sciences. Type: journal Vol. 186(2004), p.1.

Google Scholar

[2] Q. Liu, S. Y. Qin and Z. C. Li: Science & Technology Review, Vol. 28(1) (2010), p.107.

Google Scholar

[3] F. H. Long, N. N. Zheng: Journal of image and graphics, Vol. 03(7) (1998): p.592.

Google Scholar

[4] L. Itti, C. Koch: Nature Reviews Neuroscience, Vol. 2(3) (2001). p.194.

Google Scholar

[5] Laurent Itti, Christof Koch and Ernst Nieb- ur: IEEE tra- nsactions on pattern analysis and machine intelligence, Vol. 20(11)(Nov 1998), p.1254.

Google Scholar

[6] J. Shao: Microelectronics & Computer, Vol. 26(10) (2009), p.12.

Google Scholar

[7] J. J. Gao, J. Zhang and L. S. Shen: Electronic Measurement Technology, Vol. 38(6) (2008), p.1.

Google Scholar

[8] C. Koch, S. Ullman: Towards the Underlying Neural Circuitry. Human Neuro- biology, Vol. 4(6) (1985), p.219.

Google Scholar

[9] N. Sang, Z. L. Li and T. X. Zhang: Infrared and Laser Engineering, Vol. 33(1) (2004), p.38.

Google Scholar

[10] Y. R. Sun: University of Edinburgh [D] (2003).

Google Scholar

[11] P. Zhang, R. S. Wang: Journal of Software. Vol. 15(06) (2004), p.891.

Google Scholar

[12] W. Jin, J. Q. Zhang and X. Zhang: Infrared Technology. Vol. 29(12) (2007), p.720.

Google Scholar

[13] Z. H. Zeng: Xia Men University [D] (2009. 9).

Google Scholar

[14] Laurent I, GoldC and KochC: Optical Engineering [J]. Vol. 40 (2001), p.1784.

Google Scholar