Research of the Evaluation Function Based on Improved Neighborhood Difference Operator

Cheng Yun Wang; Long  Ye

doi:10.4028/www.scientific.net/AMM.427-429.1566

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 427-429Research of the Evaluation Function Based on...

Research of the Evaluation Function Based on Improved Neighborhood Difference Operator

Abstract:

Automatic focusing is one of the key technology of robot vision and digital video-systems, while play an important role in determining the quality of image. The performance of focusing depends on whether the evaluation function has unbiasedness, unimodality and noise resistance. This paper proposes a new evaluation function algorithm by improving image clarity-evaluation function of the traditional neighborhood difference operator. Compared with the existing algorithm, the results of experiments demonstrated the new algorithm has a good sensitivity, timeliness, good anti-noise ability and stability during the automatic focusing process.

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Periodical:

Applied Mechanics and Materials (Volumes 427-429)

Pages:

1566-1569

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.427-429.1566

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Online since:

September 2013

Authors:

Cheng Yun Wang*, Long Ye

Keywords:

Automatic Focusing, Evaluation Function, Neighborhood Difference Operator

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References

[1] QizhenXu. Status and Prospect of virtual instrument [J]. Electronic world，2000. 8, 4-5.

Google Scholar

[2] YufuQu. Research of vision aiming probe[D]. Thesis of Harbin Institute of Technology, (2001).

Google Scholar

[3] ChenguoJin. Study on Contrast Evaluation Function of CMOS Digital Camera [A] USA: Proceedings of the 2005 IEEE/ASME International Conference on Advanced Intelligent Techtronic [C] Monterey, 20051379- 3831.

Google Scholar

[4] Roberto Cristi1ModernDigital Signal Processing [M]. Texas LC Engineering 2003, 12-36.

Google Scholar

[5] KeYuan. Study on the theory and technology of digital imaging system for automatic focusing [D] Changsha: Changsha University, (2006).

Google Scholar

[6] WeiJiang, ZanGao. An improved automatic focusing algorithm [J]. Journal of Shandong University, 2006, 119-123.

Google Scholar

[7] HuiZhao, WeiTao. Study on automatic focusing algorithm for image measurement technology [J]. Journal of Shanghai Jiao Tong University, 2005, 121-124.

Google Scholar

[8] ZhenfengWu, HongfuZuo. Auto focuses technique of optical microscope [J]. 2000, 10-12.

Google Scholar

[9] VOLLATH. D. The influence of the scene parameters and of noise on the behavior of automatic focusing algorithms [J]. Journal of Microscopy, 1998, 151: 133-146.

DOI: 10.1111/j.1365-2818.1988.tb04620.x

Google Scholar