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Improving optical pipeline through better alignment and calibration process

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Abstract

Due to changes and movements during a measurement process, the need to have an alignment system becomes imperative, in order to avoid all possible errors that may arise from a lack of alignment. In the effort to obtain the best possible conditions for alignment, it is necessary to check whether the object to be measured is well-positioned. Good alignment reduces down-time and should be part of the quality control process. The aim of this paper is the study of light and object alignments to monitor and achieve an optimal alignment system, in order to eliminate the effects of misalignment. The algorithms were tested with a not-optimal system to ascertain its efficiency. Besides, calibration parameters that have been studied in a previous work have been added to the whole experiment in order to quantify which impact has every single optimization in the measurement error of each stage.

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References

  1. Celik O, Schulze PA, Freeman GJ, Wirth PZ, Vercesi T (2020) Methods and apparatus for absolute and relative depth measurements using camera focus distance. US Patent App. 16/669,197

  2. Chen MH, Kira Z, AlRegib G, Yoo J, Chen R, Zheng J (2019) Temporal attentive alignment for large-scale video domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp 6321–6330

  3. Chern NNK, Neow PA, Ang MH (2001) Practical issues in pixel-based autofocusing for machine vision. In: IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164), vol 3. IEEE, pp 2791–2796

  4. Dey K, Nagar S, Singh SS, Vijil EC (2020) System, method and computer program product for contextual focus/zoom of event celebrities. US Patent App. 16/668,654

  5. Fuse T, Kajihara Y (2020) 3D measurement combining multi-view and multi-focus images using light field camera. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 2:633–640

    Article  Google Scholar 

  6. Goldenberg E, Shabtay G, Avivi G, Dror M, Bachar G, Jerby I, Yedid I (2020) Auto focus and optical image stabilization in a compact folded camera. US Patent App. 16/861,866

  7. Gu Y, Liu C, Wei J, Lu K, Ji H, Zheng Y, Fan X, Wang J, Yuan Z, Gong Z (2020) Study on the dimensional metrology and alignment method for the 1/32 CFETR VV mock-up. Fusion Eng Des 155:111556

    Article  Google Scholar 

  8. Hall B (2019) An opportunity to enhance the value of metrological traceability in digital systems. In: 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT). IEEE, pp 16–21

  9. Johnson RL, Patane CJ, Whittum DH (2020) Machine vision alignment and positioning system for electron beam treatment systems. US Patent App. 16/609,966

  10. Kuster M (2020) A measurement information infrastructure’s benefits for industrial metrology and iot. In: 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT. IEEE, pp 479–484

  11. Lu Z, Cai L (2020) Camera calibration method with focus-related intrinsic parameters based on the thin-lens model. Opt Express 28(14):20858–20878

    Article  Google Scholar 

  12. Maresca P, Duarte Á, Wang C, Caja J, Gómez E (2019) Evaluation of traceability in continuous 2D measurements employing machine vision systems. Procedia Manuf 41:922–929

    Article  Google Scholar 

  13. Martin WA (2019) Method and system for assisting security camera focusing. US Patent 10,372,016

  14. Moru DK, Borro D (2020) A machine vision algorithm for quality control inspection of gears. Int J Adv Manuf Technol 106(1-2):105–123

    Article  Google Scholar 

  15. Moru DK, Borro D (2021) Analysis of different parameters of influence in industrial cameras calibration processes. Measurement 171:108750

    Article  Google Scholar 

  16. Peng L, Zhang H, Li X, Zheng S (2020) Inertial measurement system for track alignment inspection based on machine vision. In: Resilience and sustainable transportation systems. American Society of Civil Engineers Reston, pp 530–537

  17. Rivard WG, Kindle BJ, Feder AB (2019) Systems and methods for adjusting focus based on focus target information. US Patent App. 16/213,041

  18. Shylanski MS, Dorrance DR, Bernard BE, Golab TJ, Stieff MT, McClenahan JW, Silver JK, Strege TA, Colarelli III NJ (2017) Method for evaluating component calibration in machine vision vehicle wheel alignment system. US Patent 9,644,952

  19. Tropin DV, Nikolaev DP, Slugin DG (2019) The method of image alignment based on sharpness maximization. In: Eleventh International Conference on Machine Vision (ICMV 2018). International Society for Optics and Photonics, vol 11041, p 1104105

  20. Yin Y, Altmann B, Pape C, Reithmeier E (2019) Machine-vision-guided rotation axis alignment for an optomechanical derotator. Opt Lasers Eng 121:456–463

    Article  Google Scholar 

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Correspondence to Desmond K. Moru.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Author contribution

Desmond K. Moru: methodology, writing—original draft, investigation, experimentation, Vvsualization, preparation. Diego Borro: conceptualization, methodology, formal analysis, supervision, validation, writing—reviewing and editing, project administration

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Moru, D.K., Borro, D. Improving optical pipeline through better alignment and calibration process. Int J Adv Manuf Technol 114, 797–809 (2021). https://doi.org/10.1007/s00170-021-06799-9

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