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Image 3D Reconstruction Based on Binocular Vision

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DOI: 10.23977/jipta.2024.070104 | Downloads: 11 | Views: 208

Author(s)

Fang Duan 1, Wei Sun 1, Jianpeng Zhu 1

Affiliation(s)

1 College of Rail Transit, Shanghai Institute of Technology, Shanghai, 200235, China

Corresponding Author

Fang Duan

ABSTRACT

SEM can be used for studying the microstructure observation and crystal structure analysis of materials. FIB-SEM dual beam system can be simply understood as the coupling of single focused ion beam system and SEM. In order to fully utilize the grayscale information contained in microscopic morphology images, this paper proposes a three-dimensional reconstruction study based on the principle of binocular stereo vision and image processing technology. The study uses binocular vision to measure the depth information of the sample space, and combines image processing technology and visual programming technology to restore the three-dimensional morphology of the sample surface, providing data basis for a real-time feedback system for nano material processing based on FIB-SEM dual beam system images is of great significance for micro morphology observation and nano material processing.

KEYWORDS

FIB-SEM dual beam system; 3D reconstruction; Binocular stereo vision; digital image processing

CITE THIS PAPER

Fang Duan, Wei Sun, Jianpeng Zhu, Image 3D Reconstruction Based on Binocular Vision. Journal of Image Processing Theory and Applications (2024) Vol. 7: 26-31. DOI: http://dx.doi.org/10.23977/jipta.2024.070104.

REFERENCES

[1] Zhang D T, 2009. Scanning Electron Microscope and Energy Spectrometer Analysis Techniques, South China University of Technology Press, Guang Zhou
[2] Giannuzzi L A, 2005. Stevie F A. Introduction to Focused Ion Beams: Instrumentation, Theory, Techniques and Practice. Boston: Springer US, 1–12.
[3] Ahmad P. Tafti, Jessica D. Holz, et al. 3DSEM++: Adaptive and intelligent 3D SEM surface reconstruction. Micron. 2016, 87: 33-45.
[4] Zhang Guangjun, 2008. Visual Measurement. Science Press.
[5] Szeliski R, 2010. Computer vision: algorithms and applications. Springer Science business Media.
[6] Boyde A, 1973. Quantitative photogrammetric analysis and qualitative stereoscopic analysis of SEM images. Journal of Microscopy, 98 (3):452-471.
[7] Lacey A J, Thacker N A, Crossley S, Yates R B, 1998. A multi-stage approach to the dense estimation of disparity from stereo SEM images. Image and vision computing, 16 (5): 373-383.
[8] Marinello F, Bariani P, Savio E, Horsewell A, De Chiffre L,2008. Critical factors in SEM 3Dstereo microscopy Measurement Science and Technology, 19(6): 065705.
[9] Carli, L., Genta, G., Cantatore, A., Barbato, G., Chiffre, L.D., Levi, R., 2011. Uncertainty evaluation for three-dimensional scanning electron microscope reconstructions based on the stereo-pair technique. Meas. Sci. Technol. 22 (3), 035103.

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