White light interference fringe detection based on improved ViBE algorithm

Mingzhe Hu; Huipeng Li; Haowei Li; Ke Li

doi:10.1117/12.2564568

17 April 2020 White light interference fringe detection based on improved ViBE algorithm

Mingzhe Hu, Huipeng Li, Haowei Li, Ke Li

Proceedings Volume 11455, Sixth Symposium on Novel Optoelectronic Detection Technology and Applications; 114553A (2020) https://doi.org/10.1117/12.2564568
Event: Sixth Symposium on Novel Photoelectronic Detection Technology and Application, 2019, Beijing, China

Abstract

When the topography of microstructure is reconstructed by white light interference, the scanning range of the interference fringe is used to be fixed. due to the uncertainty of the angle between the object surface and the reference surface, the actual scanning range varies accordingly. In order to obtain an accurate interference scanning range, this paper proposes an improved ViBE algorithm to automatically configure the scanning range by identifying interference fringes. To solve the ghost that occurs in the traditional ViBE algorithm when detecting interference fringes, the pixel foreground life dictionary is established to record the life cycle of the foreground pixels. The static foreground points are screened out, and the background model of the static foreground points is re-initialized to achieve initial suppression of ghosts. The foreground pixel connected region is analyzed to achieve elimination of residual ghost noise. Finally, the number of foreground pixels is taken as the basis for the existence of interference fringes. The experimental results show that the improved ViBE algorithm can quickly eliminate the ghost noise in three images and obtain the accurate interference fringe binarization foreground image.

Citation Download Citation

Mingzhe Hu, Huipeng Li, Haowei Li, and Ke Li "White light interference fringe detection based on improved ViBE algorithm", Proc. SPIE 11455, Sixth Symposium on Novel Optoelectronic Detection Technology and Applications, 114553A (17 April 2020); https://doi.org/10.1117/12.2564568

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KEYWORDS

Detection and tracking algorithms

Platinum

Statistical modeling

Associative arrays

Data modeling

Lithium

Interferometers

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