Mr. Junqing Li Profile

Junqing Li

at Dalian Univ of Technology

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Proceedings Article | 5 November 2020 Paper

A classified denoising method for multi-scale measurement noise from combined visual measurement system

Yongkang Lu, Yang Zhang, Junqing Li, Ziliang Tan, Wenqi Wang, Haiyang Zhao, Wei Liu

Proceedings Volume 11567, 115670E (2020) https://doi.org/10.1117/12.2574423

KEYWORDS: Clouds, Denoising, Digital filtering, Signal to noise ratio, Image segmentation, Optical filters, Visualization, Data acquisition, Principal component analysis

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The assembly gap between components is very vital for the evaluation of assembly quality of aircrafts. Due to the limits of gap size and operation space, the assembly gap needs to be indirectly calculated by the measurements of surface of components instead of plug gauge test. However, the surface constituted of point cloud is usually mixed with different types of noise ,which severely affects the evaluation of assembly gap. To remove these different types of noise simultaneously with high efficiency, a classified denoising method combining with an improved bilateral filtering and median filtering was proposed. Firstly, based on the principal component analysis, a new coordinate system was established to achieve the homogeneity of coordinates of point cloud. Then, an improved median filtering method on the basis of region segmentation (RSMF) was used to remove large-scale noise. Next, the fast bilateral filtering method based on threshold segmentation (TSBF) was proposed to remove small-scale noise. Finally, a measurement experiment of aircraft component was performed to verify the effectiveness of the proposed method. Experimental results showed that the proposed method could not only reduce measurement error including RMSE (Root Mean Square Error), but also improve SNR (Signal Noise Ratio) and PSNR (Peak Signal to Noise Ratio) of point cloud data.

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