Skip to main content

Rock-Ring Accuracy Improvement in Infrared Satellite Image with Subpixel Edge Detection

  • Conference paper
  • First Online:
Image and Graphics Technologies and Applications (IGTA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 875))

Included in the following conference series:

  • 1880 Accesses

Abstract

The projection of space circle can be utilized to relative pose measurement of satellite targets. The accuracy of the ellipse parameter is crucial to the pose recovery precision. However, the image quality of space visible image and infrared image are poor. The conventional ellipse detection methods are mainly based on pixel-accuracy-wise edges and the detection accuracy are low which leads to errors in pose recovery. In this paper, a subpixel-accuracy-wise edges based fitting method is proposed to improve the ellipse accuracy. To realize this goal, we design ellipse based subpixel edge detection method. Experimental results show that the ellipse accuracy fitted by subpixel edge coordinate is higher than by pixel edge coordinate, especially when the ellipse is incomplete. Our method is the first one that present and validate that the subpixel edge coordinate is contribute to enhancing ellipse detection accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liu, C., Hu, W.: Relative pose estimation for cylinder-shaped spacecrafts using single image. IEEE Trans. Aerosp. Electron. Syst. 50(4), 3036–3056 (2014)

    Article  Google Scholar 

  2. Zhang, H., Jiang, Z., Elgammal, A.: Satellite recognition and pose estimation using homeomorphic manifold analysis. IEEE Trans. Aerosp. Electron. Syst. 51(1), 785–792 (2015)

    Article  Google Scholar 

  3. Opromolla, R., et al.: Pose estimation for spacecraft relative navigation using model-based algorithms. IEEE Trans. Aerosp. Electron. Syst. PP(99), 1 (2017)

    Google Scholar 

  4. Tzschichholz, T.: Passive satellite pose estimation based on PMD/CCD sensor data fusion (2015)

    Google Scholar 

  5. Liu, L., Zhao, G., Bo, Y.: Point cloud based relative pose estimation of a satellite in close range. Sensors 16(6), 824 (2016)

    Article  Google Scholar 

  6. Shiu, Y.C., Ahmad, S.: 3D location of circular and spherical features by monocular model-based vision. In: IEEE International Conference on Systems, Man and Cybernetics, 1989. Conference Proceedings, vol. 2, pp. 576–581. IEEE (2002)

    Google Scholar 

  7. Zheng, Y., Ma, W., Liu, Y.: Another way of looking at monocular circle pose estimation. IEEE In: International Conference on Image Processing, pp. 861–864. IEEE (2008)

    Google Scholar 

  8. Wang, G., Wu, J., Ji, Z.: Single view based pose estimation from circle or parallel lines. Pattern Recogn. Lett. 29(7), 977–985 (2008)

    Article  Google Scholar 

  9. Miao, X., Zhu, F.: Monocular vision pose measurement based on docking ring component. Acta Optica Sinica 33(4), 0412006 (2013)

    Article  Google Scholar 

  10. Zhang, L., et al.: Improvement of position and orientation measurement algorithm of monocular vision based on circle features. J. Hefei Univ. Technol. 32(11), 1669–1673 (2009)

    Google Scholar 

  11. Lu, W., Jinhua, Yu., Tan, J.: Direct inverse randomized Hough transform for incomplete ellipse detection in noisy images. J. Pattern Recognit. Res. 1, 13–24 (2014)

    Article  Google Scholar 

  12. Rueda, S., et al.: Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans. Med. Imaging 33(4), 797–813 (2014)

    Article  Google Scholar 

  13. Yao, P., Evans, G., Calway, A.: Using affine correspondence to estimate 3-D facial pose. In: Proceedings of the 2001 International Conference on Image Processing, vol. 3. IEEE (2001)

    Google Scholar 

  14. Luckett, J.A.: Comparison of Three Machine Vision Pose Estimation Systems Based on Corner, Line, and Ellipse Extraction for Satellite Grasping. West Virginia University, Morgantown (2012)

    Google Scholar 

  15. Matei, B., Meer, P.: Reduction of bias in maximum likelihood ellipse fitting. In: Proceedings of the 15th International Conference on Pattern Recognition, 2000, vol. 3. IEEE (2000)

    Google Scholar 

  16. Fitzgibbon, A., Pilu, M., Fisher, R.B.: Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 476–480 (1999)

    Article  Google Scholar 

  17. Liu, C., Weiduo, H.: Ellipse fitting for imaged cross sections of a surface of revolution. Pattern Recognit. 48(4), 1440–1454 (2015)

    Article  Google Scholar 

  18. Wong, C.Y., et al.: A survey on ellipse detection methods. In: 2012 IEEE International Symposium on Industrial Electronics (ISIE). IEEE (2012)

    Google Scholar 

  19. Nguyen, T.M., Ahuja, S., Wu, Q.M.J.: A real-time ellipse detection based on edge grouping. In: IEEE International Conference on Systems, Man and Cybernetics, 2009, SMC 2009. IEEE (2009)

    Google Scholar 

  20. Qiao, Yu., Ong, S.H.: Arc-based evaluation and detection of ellipses. Pattern Recognit. 40(7), 1990–2003 (2007)

    Article  Google Scholar 

  21. Kim, E., Haseyama, M., Kitajima, H.: Fast and robust ellipse extraction from complicated images. In: Proceedings of IEEE Information Technology and Applications (2002)

    Google Scholar 

  22. Mai, F., et al.: A hierarchical approach for fast and robust ellipse extraction. Pattern Recognit. 41(8), 2512–2524 (2008)

    Article  Google Scholar 

  23. Chia, A.Y.S., et al.: A split and merge based ellipse detector with self-correcting capability. IEEE Trans. Image Process. 20(7), 1991–2006 (2011)

    Article  MathSciNet  Google Scholar 

  24. Cakir, H.I., Benligiray, B. and Topal, C.: Combining feature-based and model-based approaches for robust ellipse detection. In: 2016 24th European Signal Processing Conference (EUSIPCO). IEEE (2016)

    Google Scholar 

  25. Prasad, D.K., Leung, M.K.H., Quek, C.: ElliFit: an unconstrained, non-iterative, least squares based geometric ellipse fitting method. Pattern Recognit. 46(5), 1449–1465 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Huan Zhang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, H., Meng, C., Li, Z. (2018). Rock-Ring Accuracy Improvement in Infrared Satellite Image with Subpixel Edge Detection. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_18

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1702-6_18

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1701-9

  • Online ISBN: 978-981-13-1702-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics