Research on the centroid detecting accuracy of stripe

Yujiao Guo; Lin Li; Qiudong Zhu; Shanshan Wang

doi:10.1117/12.2285268

24 October 2017 Research on the centroid detecting accuracy of stripe

Yujiao Guo, Lin Li, Qiudong Zhu, Shanshan Wang

Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 104623T (2017) https://doi.org/10.1117/12.2285268
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China

Abstract

The centroid detecting accuracy of the stripe has an important influence on reconstruction and configuration of object structure in structured light 3D scanning measurement system. This paper analyzes the influence of several factors on the accuracy of stripe centroid detection. The pictures are grouped according to the peak signal-to-noise ratio and the stripe width. The gray-scale matrix is read out by Matlab, and then the centroid position is calculated by using the gradient centroid algorithm for the gray scale image by subtracting the threshold from the window. Finally, the accuracy of each group of images is evaluated by its standard deviation. The standard deviation of the group image is taken as the evaluation criterion, and the influence of PSNR and the stripe width on the accuracy of centroid detection is obtained. The experimental and simulation results show that the narrower stripes are more able to achieve higher accuracy; the accuracy is improved as the signal to noise ratio increases and multi-frame stacking can significantly improve the detection accuracy. The variation curves of the factors on the accuracy are given in the text. At the same time, the phenomenon that the measured value is fluctuating under a peak signal-to-noise ratio is analyzed by simulation. The results show that the fluctuation is consistent with the normal distribution.

Citation Download Citation

Yujiao Guo, Lin Li, Qiudong Zhu, and Shanshan Wang "Research on the centroid detecting accuracy of stripe", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104623T (24 October 2017); https://doi.org/10.1117/12.2285268

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KEYWORDS

Signal to noise ratio

Superposition

Detection and tracking algorithms

Sensors

CMOS sensors

Interference (communication)

MATLAB

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