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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)

Abstract

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.

Keywords

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.

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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

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