A Rapid and Automatic Feature Extraction Method for Artificial Targets Used in Industrial Photogrammetry Applications

Jian Wei Liu; Zhi Qiang Jiang; Hao Hu; Xin Yin

doi:10.4028/www.scientific.net/AMM.170-173.2995

Paper Titles

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Partial Correlation Analysis of Color Reflectance of Dynamic Moisture Transfer of Fabric and Static Physical Indicators of Fabric
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A Rapid and Automatic Feature Extraction Method for Artificial Targets Used in Industrial Photogrammetry Applications
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 170-173A Rapid and Automatic Feature Extraction Method...

A Rapid and Automatic Feature Extraction Method for Artificial Targets Used in Industrial Photogrammetry Applications

Abstract:

Distributing artificial targets on the object to be measured is a reliable and common method for achieving optimum target location and accurate correspondence among multi-view images, which are universally adopted in industrial photogrammetry applications. In this paper artificial circular un-coded targets and coded targets are used as reference points, an automatic and rapid algorithm for reference point detection is proposed. Targets are extracted from the images according to their size, shape, intensity , etc. An improved method to identify the ID of the coded target is proposed. The gray scale centroid algorithm is applied to get sub-pixel locations of both un-coded and coded targets. Practical examples show that the algorithm can identify and locate artificial targets in images quickly and accurately. It is robust to the change of projection angles and noise.

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Periodical:

Applied Mechanics and Materials (Volumes 170-173)

Pages:

2995-2998

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.170-173.2995

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Online since:

May 2012

Authors:

Jian Wei Liu*, Zhi Qiang Jiang, Hao Hu, Xin Yin

Keywords:

Artificial Targets, Canny Edge Detection, Coarsely Localization, Coded Targets Recognition, Ellipse Fitting

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References

[1] Xie, Y. and Q. Ji, A new efficient ellipse detection method. Pattern Recognition, 2002. 2: p.20957.

Google Scholar

[2] Y. Lei, K. C. Wong, Ellipse detection based on symmetry, Pattern Recog. Lett. 20, 1999, 41—47.

Google Scholar

[3] S. Tsuji, F. Matsumoto, Detection of ellipses by a modified Hough transform, IEEE Trans. On Computers 27(8), 1978, 777-781.

DOI: 10.1109/tc.1978.1675191

Google Scholar

[4] P. Yin, L. Chen, New method for ellipse detection by means for symmetry, Journal of Electronic Imaging.3(1), 1994, 20-29.

Google Scholar

[5] Qiang Ji and Robert M. Haralick, A Statistically Efficient Method for Ellipse Detection, in 1999 IEEE International Conference on Image Processing, Kobe, Japan, October, 1999.

DOI: 10.1109/icip.1999.822992

Google Scholar

[6] Ahn, S.J., W. Rauh, and M. Recknagel. Circular coded landmark for optical 3D-measurement and robot vision. 1999: IEEE.

DOI: 10.1109/iros.1999.812831

Google Scholar

[7] Canny, J., A computational approach to edge detection. Readings in computer vision: issues, problems, principles, and paradigms, 1987. 184: pp.87-116.

DOI: 10.1016/b978-0-08-051581-6.50024-6

Google Scholar