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The image stitching algorithm based on aggregated star groups

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Abstract

The star map has the characteristics of large amount of data and wide angle of view. The star points are displayed as limited pixels on the image and easy to get disturbed by noise, so it is difficult to build a feature model to obtain the complete star map. In this paper, we have revised the OTSU algorithm and extracted the star points accurately according to the characteristics of star point pixels. We are inspired by the idea of compressed perception to break the local spatial relationship of stars, construct the aggregated star group model of relative relationship of local stars, realize the matching and construct the extended model to realize the stitching of wide angle star images. In order to meet the requirements of efficient transmission and storage in the air or on the ground, we have proposed the algorithm of star point storage and mapping. We set up a database with 600 frames of real star images from CCD camera and 600 frames of simulated star images, and the experiments show that our algorithm can compress the data and realize the star image stitching.

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References

  1. Demir, H.S., Cetin, A.E.: Co-difference based object tracking algorithm for infrared videos. In: 2016 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 434–438 (2016)

  2. Sengar, S.S., Mukhopadhyay, S.: Moving object detection based on frame difference and W4. SIViP 11(7), 1357–1364 (2017)

    Article  Google Scholar 

  3. Yan, J., Jie, J., Guangjun, Z.: Star extraction method for high dynamic star sensor. Infrared Laser Eng. 40(11), 2281–2285 (2011)

    Google Scholar 

  4. Dingbang, P., Hao, C., Xiaochong, S., et al.: Motion blurred star image centroid optimized extraction based on prior Gaussian distribution. In: Control And Decision Conference (CCDC), 2017 29th Chinese. IEEE, pp. 3149–3154 (2017)

  5. Zhang, R., Liu, X., Hu, J., et al.: A fast method for moving object detection in video surveillance image. SIViP 11(5), 841–848 (2017)

    Article  Google Scholar 

  6. Zhou, F., Zhao, J., Ye, T., et al.: Fast star centroid extraction algorithm with sub-pixel accuracy based on FPGA. J. Real-Time Image Proc. 12(3), 613–622 (2016)

    Article  Google Scholar 

  7. Haiyong, W., Wenrui, Z., Cheng, X., et al.: Image smearing modeling and verification for strapdown star sensor. Chin. J. Aeronaut. 25(1), 115–123 (2012)

    Article  Google Scholar 

  8. Tang, Y., Li, J., Wang, G.: Spacecraft angular velocity estimation algorithm for star tracker based on optical flow techniques. Opt. Eng. 57(2), 023101 (2018)

    Article  Google Scholar 

  9. Gao, Y., Qin, S., Wang, X.: Adaptive iteration method for star centroid extraction under highly dynamic conditions. In: International Symposium on Optoelectronic Technology and Application 2016. International Society for Optics and Photonics, pp. 1015718–1015718-7 (2016)

  10. Zhang, J., Wang, J.: An enhanced Cramér-Rao bound weighted method for attitude accuracy improvement of a star tracker. Rev. Sci. Instrum. 87(6), 063112 (2016)

    Article  Google Scholar 

  11. Han, J., Wang, C., Li, B.: A novel optimization method for star-sensor data processing. Optik Int. J. Light Electron Opt. 132, 192–202 (2017)

    Article  Google Scholar 

  12. Liu, D., Xiong, Y., Li, Y., et al.: Weak point target detection in star sensor. In: Selected Proceedings of the Chinese Society for Optical Engineering Conferences held July 2016. International Society for Optics and Photonics, pp. 101410C–101410C-5 (2016)

  13. Sun, T., Xing, F., Wang, X., et al.: Effective star tracking method based on optical flow analysis for star trackers. Appl. Opt. 55(36), 10335–10340 (2016)

    Article  Google Scholar 

  14. Alouaoui, H., Turki, S.Y., Faïz, S.: A neural network based on time series for spatiotemporal relationships prediction. Int. J. Spat. Temporal Multimed. Inf. Syst. 1(1), 63–86 (2016)

    Google Scholar 

  15. Zhu, H., Liang, B., Zhang, T.: High accuracy star centroid acquisition method of airborne star sensor during daytime. In: 2016 12th World Congress on Intelligent Control and Automation (WCICA), IEEE, pp. 240–245 (2016)

  16. Duan, Y., Niu, Z., Chen, Z.: A star identification algorithm for large FOV observations. In: SPIE Remote Sensing. International Society for Optics and Photonics, pp. 100041G–100041G-9 (2016)

  17. Wang, Y., Zhang, H.: Star recognition based on mixed star pattern and multilayer SOM neural network. In: Aerospace Conference, 2017 IEEE. IEEE, pp. 1–6 (2017)

  18. Wang, G., Li, J., Wei, X.: Star Identification based on Hash Map. IEEE Sens. J. 18, 1591–1599 (2017)

    Article  Google Scholar 

  19. Han, J., Wang, C., Li, B.: High accuracy method of positioning based on multi-star-sensor. In: 2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, pp. 236–242 (2016)

  20. Rousseau, G.L.A., Bostel, J., Mazari, B.: Star recognition algorithm for APS star tracker: oriented triangles. IEEE Aerosp. Electron. Syst. Mag. 20(2), 27–31 (2005)

    Article  Google Scholar 

  21. Zong, H., Gao, X.Y., Wang, B.: Modification and implementation of a triangle star pattern recognition algorithm. In: Control Conference (CCC), 2013 32nd Chinese. IEEE, pp. 5182–5186 (2013)

  22. Fan, Q., Zhong, X.: A triangle voting algorithm based on double feature constraints for star sensors. Adv. Space Res. 61, 1132–1142 (2017)

    Article  Google Scholar 

  23. Cole, C.L., Crassidus, J.L.: Fast Star Pattern Recognition Using Spherical Triangles. State University of New York at Buffalo, Buffalo (2004)

    Book  Google Scholar 

  24. Jiang, D., Zhang, K., Debeir, O.: Star pattern recognition based on features invariant under rotation. Pattern Recognit. Image Anal. 27(3), 532–537 (2017)

    Article  Google Scholar 

  25. Hernández, E.A., Alonso, M.A., Chávez, E., et al.: Robust polygon recognition method with similarity invariants applied to star identification. Adv. Space Res. 59(4), 1095–1111 (2017)

    Article  Google Scholar 

  26. Aghaei, M., Moghaddam, H.A.: Grid star identification improvement using optimization approaches. IEEE Trans. Aerosp. Electron. Syst. 52(5), 2080–2090 (2016)

    Article  Google Scholar 

  27. Mortari, D., Samaan, M.A., Bruccoleri, C., et al.: The pyramid star identification technique. Navigation 51(3), 171–183 (2004)

    Article  Google Scholar 

  28. Zhang, G.: Star Identification Utilizing Neural Networks [M]//Star Identification, pp. 153–176. Springer, Berlin Heidelberg (2017)

    Google Scholar 

  29. Mehta, D.S., Chen, S., Low, K.S.: A robust star identification algorithm with star shortlisting. Adv. Space Res. 61(10), 2647–2660 (2018)

    Article  Google Scholar 

  30. Fan, Q., Zhong, X., Sun, J.: A voting-based star identification algorithm utilizing local and global distribution. Acta Astronaut. 144, 126–135 (2018)

    Article  Google Scholar 

  31. Li, J., Wei, X., Zhang, G.: Iterative algorithm for autonomous star identification. IEEE Trans. Aerosp. Electron. Syst. 51(1), 536–547 (2015)

    Article  Google Scholar 

  32. Zhao, C.G., Tan, J.B., Liu, J., et al.: Star simulator for testing celestial navigation equipment. Opt. Precis. Eng. 18(6), 1326–1332 (2010)

    Google Scholar 

  33. Hao, Y.: Design of light source system and optical system for a static star simulator. Int. J. Smart Home 9(11), 91–98 (2015)

    Article  Google Scholar 

  34. Sun, G.F., Zhang, G.Y., Wang, L.Y., et al.: Compact star simulator with very high accuracy. Proceedings of SPIE 8417, 84171J-1 (2015)

    Google Scholar 

  35. Gao, X., Gan, X., Ma, X.: Study of digital high-precision multi-star simulator for multi-magnitude output. Int. J. Control Autom. 9(9), 255–264 (2016)

    Article  Google Scholar 

  36. Wang, H., Wang, Y., Li, Z., et al.: Systematic centroid error compensation for the simple Gaussian PSF in an electronic star map simulator. Chin. J. Aeronaut. 27(4), 884–891 (2014)

    Article  Google Scholar 

  37. Wei, Z.L., Liu, W.Q., Liu, H.: Optical design of visual simulation lens for dynamic target simulator. J. Appl. Opt. 1, 007 (2013)

    Google Scholar 

  38. Gan, X.J., Gao, X.H., Chen, Y., et al.: Design of single star simulator based on LED light source. Appl. Mech. Mater. Trans. Tech. Publ. 397, 932–935 (2013)

    Article  Google Scholar 

  39. Liu, H., Su, D., Tan, J., et al.: An approach to star image simulation for star sensor considering satellite orbit motion and effect of image shift. J. Astronaut. 32, 1190–1194 (2011)

    Google Scholar 

  40. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  41. Sa, J., Sun, X., Zhang, T., et al.: Improved Otsu segmentation based on sobel operator. In: 2016 3rd International Conference on Systems and Informatics (ICSAI). IEEE, pp. 886–890 (2016)

  42. Hao, D., Li, Q., Li, C.: Histogram-based image segmentation using variational mode decomposition and correlation coefficients. SIViP 11(8), 1411–1418 (2017)

    Article  Google Scholar 

  43. Di, Z., Zhelei, X.I.A.: An improved Otsu threshold segmentation algorithm. J. China Univ. Metrol. 3, 014 (2016)

    Google Scholar 

  44. Malik, P., Karthik, K.: Iterative content adaptable purple fringe detection. SIViP 12(1), 181–188 (2018)

    Article  Google Scholar 

  45. Kai, H., Xianmin, M.: Real-time monitoring for the mining robot based on an improved SIFT matching algorithm. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE, pp 1–5 (2017)

  46. Wang, M., Niu, S., Yang, X.: A novel panoramic image stitching algorithm based on ORB. In: 2017 International Conference on Applied System Innovation (ICASI). IEEE, pp. 818–821 (2017)

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Acknowledgements

This work is supported by Light of West China of Chinese Academy of Sciences (XAB2016B23). The Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences (LSIT201717G).

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Authors and Affiliations

  1. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, 710119, People’s Republic of China

    Shi Qiu & Yun Du

  2. College of Information Science and Technology, Chengdu University of Technology, Chengdu, 610059, People’s Republic of China

    Dongmei Zhou

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  1. Shi Qiu
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  2. Dongmei Zhou
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Correspondence to Dongmei Zhou.

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Qiu, S., Zhou, D. & Du, Y. The image stitching algorithm based on aggregated star groups. SIViP 13, 227–235 (2019). https://doi.org/10.1007/s11760-018-1349-y

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