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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 852))

Abstract

The paper presents a semi-automatic image registration method. It includes a discussion of the most frequently used image registration methods and an analysis of the effectiveness of the registration of images of the tested object. The tested object was a power line supporting structure. For the purposes of the study, a series of photographs of this object were taken and acquired with images generated on the basis of a 3D model of the scene in a CAD environment. The main objective of the image registration performed was to allow the comparison of the actual condition of the structure (e.g. resulting from vision inspection performed by means of flying machines) with the design documentation.

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References

  1. Zitová, B., Flusser, J.: Image registration methods: a survey. Image Vision Comput. 21(11), 977–1000 (2003)

    Article  Google Scholar 

  2. Vemuri, B., Ye, J., Chen, Y., Leonard, C.: Image registration via level-set motion: applications to atlas-based segmentation. Medical Image Analysis, March 2003

    Google Scholar 

  3. Ardeshir, A.: 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications. Wiley Press (2005)

    Google Scholar 

  4. Gasz, R., Zator, S.: Evaluation of selected elements of a power line with using CAD environment. In: Tomczuk, B., Waindok, A., Zimon, J., Wajnert, D. (eds.) Electrodynamic and Mechatronic Systems SELM 2013, pp. 47–48 (2013). https://doi.org/10.1109/SELM.2013.6562973

  5. Zator, S., Gasz, R.: Ocena stanu konstrukcji wsporczych na podstawie zdjęć. Pomiary Automatyka Robotyka 4/2016, pp. 23–26 (2016). ISSN 1427-9126. https://doi.org/10.14313/PAR_222/23

  6. Fischer, B., Modersitzki, J.: Ill-posed medicine – an introduction to image registration. Inverse Prob. 24, 1–19 (2008)

    Article  MathSciNet  Google Scholar 

  7. Xie, J., Hsu, Y., Feris, R., Sun, M.: Fine registration of 3D point clouds with iterative closest point using an RGB-D camera. In: Proceedings of the 2013 IEEE International Symposium on Circuits and Systems, Melbourne, Australia (2013)

    Google Scholar 

  8. Huber, D., Hebert, M.: Fully automatic registration of multiple 3D data sets. Image Vis Comput (2001)

    Google Scholar 

  9. Takimoto, R., de Sales, M.: 3D reconstruction and multiple point cloud registration using a low precision RGB-D sensor. Mechatronics 35 (2016)

    Google Scholar 

  10. Chen, C., Stamos, I.: Semi-automatic range to range registration: a feature-based method. In: International Conference on 3-D Digital Imaging and Modeling (2005)

    Google Scholar 

  11. Goshtasby, A.: Image registration by local approximation methods. Image Vis. Comput. 6, 255–261 (1988)

    Article  Google Scholar 

  12. Karsli, F., Dihkan, M.: Determination of geometric deformations in image registration using geometric and radiometric measurements. Sci. Res. Essays 5(3), 260–274 (2010)

    Google Scholar 

  13. Berman, M., Blaschko, M.: Optimization of the Jaccard index for image segmentation with the Lovász hinge. In: CVPR 2018 (2018)

    Google Scholar 

  14. Yoav, B.: Simultaneous and selective inference: current successes and future challenges. Biometrical J. 52(6), 708–721 (2010). https://doi.org/10.1002/bimj.200900299

    Article  MathSciNet  MATH  Google Scholar 

  15. Alison, S.: Difference Between Accuracy And Precision. Englishtipsdaily.com. Accessed 5 Aug 2016

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Correspondence to Rafał Gasz .

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Gasz, R., Ruszczak, B., Tomaszewski, M., Zator, S. (2019). The Registration of Digital Images for the Truss Towers Diagnostics. In: Borzemski, L., Świątek, J., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018. ISAT 2018. Advances in Intelligent Systems and Computing, vol 852. Springer, Cham. https://doi.org/10.1007/978-3-319-99981-4_16

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