A Recursive Least Squares Solution for Recovering Robust Planar Homographies

  • Saad Ali Imran
  • Nabil Aouf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6856)


Presented is a recursive least squares (RLS) solution for estimating planar homographies between overlapping images. The use of such a technique stems from its ability in dealing with corrupted and periodic measurements to provide the best solution. Furthermore, its capacity for providing reliable results for time varying parameter estimation also motivates its use in the context of real time cooperative image mosaicing where optimal transformation between mobile platforms is likely to change due to motion and varying ambient conditions and thus a way to tackle this problem real time is what is required. Additionally, and within the same context, a derived ”match making” algorithm is introduced based on high curvature points (Harris points) and 3D intensity histograms which are in-turn matched using the L 2 and L  ∞  and then compared to classical cross correlation(CC) techniques.

Experimental results show that for synthetic data heavily corrupted by noise the RLS does a decent job of finding an improved homography, provided that the initial estimate is good. Results from real image data show similar results where the homography estimate is improved upon by periodic measurements. The match making algorithm proposed fairs well compared to intensity vector techniques, with the L  ∞  based method coming out on top.


Cross Correlation Root Means Square Initial Estimate Recursive Little Square Image Mosaic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    System identification: Theory for the user, 2nd edn. Prentice Hall PTR, Englewood Cliffs (1999)Google Scholar
  2. 2.
    Brown, L.: A survey of image registration techniques. ACM Computing Surveys 24, 325–376 (1992)CrossRefGoogle Scholar
  3. 3.
    Haris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–151 (1998)Google Scholar
  4. 4.
    Hartley, R., Zisserman, A.: Multiple view geometry, 2nd edn. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  5. 5.
    Hoshino, J., Kourogi, M.: Fast panoramic image mosaicing using 1d flow estimation. Real-Time Imaging 8, 95–103 (2002)CrossRefzbMATHGoogle Scholar
  6. 6.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal for Computer Vision 2, 91–110 (2004)CrossRefGoogle Scholar
  7. 7.
    Mikolajczyk, K., Shmid, C.: A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence 27 (2005)Google Scholar
  8. 8.
    Nemra, A., Aouf, N.: Robust feature extraction and correspondence for uav map building. In: 17th MED Conference (2009)Google Scholar
  9. 9.
    Ping, Y., Zheng, M., Anjie, G., Feng, Q.: Video image mosaics in real-time based on sift. In: Conference on Pervasive Computing Signal Processing and Applications (2002)Google Scholar
  10. 10.
    Santos, C., Stoeter, S., Rybski, P., Papanikolopoulos, N.: Mosaicking images: Panoramic imaging for miniature robots. IEEE Robotics and Automation Magazine, 62–68 (2004)Google Scholar
  11. 11.
    Saptharishi, M., Oliver, C., Diehl, C., Bhat, K., Dolan, J., Ollennu, A., Khosla, P.: Distributed surveillance and reconissaince using multiple autonomous atvs: Cyberscout. IEEE Robotics and Automation Magazine 18 (2002)Google Scholar
  12. 12.
    Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. Internation Journal of Computer Vision 37(151-172) (2000)Google Scholar
  13. 13.
    Zhang, Z., Deriche, R., Faugeras, O., Luong, Q.: A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence 78, 87–119 (1995)CrossRefGoogle Scholar
  14. 14.
    Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 0–977 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Saad Ali Imran
    • 1
  • Nabil Aouf
    • 1
  1. 1.Cranfield Defence and Security, Department of Informatics and Systems Engineering, Sensors GroupCranfield UniversityShrivenhamUK

Personalised recommendations