Uncertainty Model for Template Feature Matching

  • Hongmou Zhang
  • Denis Grießbach
  • Jürgen Wohlfeil
  • Anko Börner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10749)


Using visual odometry and inertial measurements, indoor and outdoor positioning systems can perform an accurate self-localization in unknown, unstructured environments where absolute positioning systems (e.g. GNSS) are unavailable. However, the achievable accuracy is highly affected by the residuals of calibration, the quality of the noise model, etc. Only if these unavoidable uncertainties of sensors and data processing can be taken into account and be handled via error propagation, which allows to propagate them through the entire system. The central filter (e.g. Kalman filter) of the system can then make use of the enhanced statistical model and use the propagated errors to calculate the optimal result. In this paper, we focus on the uncertaintiy calculation of the elementary part of the optical navigation, the template feature matcher. First of all, we propose a method to model the image noise. Then we use Taylor’s theorem to extend two very popular and efficient template feature matchers sum-of-absolute-differences (SAD) and normalized-cross-correlation (NCC) to get sub-pixel matching results. Based on the proposed noise model and the extended matcher, we propagate the image noise to the uncertainties of sub-pixel matching results. Although the SAD and NCC are used, the image noise model can be easily combined with other feature matchers. We evaluate our method by an Integrated Positioning System (IPS) which is developed by German Aerospace Center. The experimental results show that our method can improve the quality of the measured trajectory. Moreover, it increases the robustness of the system.


Uncertainty model Image noise model Template matching Propagation of uncertainty Sub-pixel matching 


  1. 1.
    Alsaade, F.: Fast and accurate template matching algorithm based on image pyramid and sum of absolute difference similarity measure. Res. J. Inf. Technol. 4(4), 204–211 (2012)Google Scholar
  2. 2.
    Amerini, I., Caldelli, R., Cappellini, V., Picchioni, F., Piva, A.: Estimate of PRNU noise based on different noise models for source camera identification. IJDCF 2(2), 21–33 (2010)Google Scholar
  3. 3.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). CrossRefGoogle Scholar
  4. 4.
    Bloesch, M., Omari, S., Hutter, M., Siegwart, R.: Robust visual inertial odometry using a direct EKF-based approach. In: Intelligent Robots and Systems (2015)Google Scholar
  5. 5.
    Boyat, A.K., Joshi, B.K.: A review paper: noise models in digital image processing. Sig. Image Process.: Int. J. 6(2), 63–75 (2015)Google Scholar
  6. 6.
    Brunelli, R.: Template Matching Techniques in Computer Vision: Theory and Practice. Wiley, Hoboken (2009)CrossRefGoogle Scholar
  7. 7.
    Clifford, A.: Multivariate Error Analysis: A Handbook of Error Propagation and Calculation in Many-Parameter Systems. Wiley, Hoboken (1973)Google Scholar
  8. 8.
    Evtikhiev, N.N., Starikov, S.N., Cheryomkhin, P.A., Krasnov, V.V.: Measurement of noises and modulation transfer function of cameras used in optical-digital correlators. International Society for Optics and Photonics (2012)Google Scholar
  9. 9.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Inc., Upper Saddle River (2006)Google Scholar
  10. 10.
    Grießbach, D.: Stereo-vision-aided inertial navigation. Ph.D. thesis, Freie Universitt Berlin (2014)Google Scholar
  11. 11.
    Grießbach, D., Baumbach, D., Zuev, S.: Stereo-vision-aided inertial navigation for unknown indoor and outdoor environments. In: 2014 IPIN (2014)Google Scholar
  12. 12.
    Haralick, R., Shapiro, L.: Computer and Robot Vision, vol. 2. Addison-Wesley Publishing Company, Boston (1993)Google Scholar
  13. 13.
    Holst, G.C.: CCD Arrays, Cameras, and Displays, 2nd edn. Society of Photo Optical, Bellingham (1998)Google Scholar
  14. 14.
    Jayaraman: Digital Image Processing, 1st edn. Mc Graw Hill India, New Delhi (2009)Google Scholar
  15. 15.
    Kanatani, K.I.: Uncertainty modeling and model selection for geometric inference. IEEE Trans. Pattern Anal. Mach. Intell. 26(10), 1307–1319 (2004)CrossRefGoogle Scholar
  16. 16.
    Kanazawa, Y., Kanatani, K.: Do we really have to consider covariance matrices for image features? Electron. Commun. Jpn. 86, 1–10 (2003)Google Scholar
  17. 17.
    Kim, K.B., Kim, J.S., Choi, J.S.: Fourier based image registration for sub-pixel using pyramid edge detection and line fitting. In: Intelligent Networks and Intelligent Systems. IEEE (2008)Google Scholar
  18. 18.
    Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: ICCV. IEEE (2011)Google Scholar
  19. 19.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  20. 20.
    Lowry, S., Sunderhauf, N., Newman, P., Leonard, J.J., Cox, D., Corke, P., Milford, M.J.: Visual place recognition: a survey. IEEE Trans. Robot. 32(1), 1–19 (2016)CrossRefGoogle Scholar
  21. 21.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, IJCAI 1981, vol. 2 (1981)Google Scholar
  22. 22.
    Madsen, K., Nielsen, H.B., Tingleff, O.: Methods for Non-linear Least Squares Problems (1999)Google Scholar
  23. 23.
    Mair, E., Hager, G.D., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 183–196. Springer, Heidelberg (2010). CrossRefGoogle Scholar
  24. 24.
    Mourikis, A.I., Roumeliotis, S.I.: A multi-state constraint Kalman filter for vision-aided inertial navigation. In: Proceedings IEEE ICRA (2007)Google Scholar
  25. 25.
    Nakamura, J.: Image Sensors and Signal Processing for Digital Still Cameras. Optical Science and Engineering. CRC Press, Boca Raton (2016)Google Scholar
  26. 26.
    Rosten, E., Porter, R., Drummond, T.: Faster and better: a machine learning approach to corner detection. Pattern Anal. Mach. Intell. 32(1), 105–119 (2010)CrossRefGoogle Scholar
  27. 27.
    Sheorey, S., Keshavamurthy, S., Yu, H., Nguyen, H., Taylor, C.N.: Uncertainty estimation for KLT tracking. In: Jawahar, C.V., Shan, S. (eds.) ACCV 2014. LNCS, vol. 9009, pp. 475–487. Springer, Cham (2015). Google Scholar
  28. 28.
    Shi, J., Tomasi, C.: Good features to track. In: CVPR (1994)Google Scholar
  29. 29.
    Stein, S., Jones, J.: Modern Communication Principles: With Application to Digital Signaling. McGraw-Hill, New York City (1967)Google Scholar
  30. 30.
    Thevenaz, P., Ruttimann, U.E., Unser, M.: A pyramid approach to subpixel registration based on intensity. IEEE Trans. Image Process. 7(1), 27–41 (1998)CrossRefGoogle Scholar
  31. 31.
    Zeisl, B., Georgel, P.F., Schweiger, F., Steinbach, E.G., Navab, N., Munich, G.: Estimation of location uncertainty for scale invariant features points. In: BMVC (2009)Google Scholar
  32. 32.
    Zhang, H., Wohlfeil, J., Grießbach, D.: Extension and evaluation of the AGAST feature detector. In: XXIII ISPRS Congress Annals 2016, vol. 3, pp. 133–137 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.German Aerospace CenterBerlinGermany

Personalised recommendations