Automatic Classification of Image Registration Problems

  • Steve Oldridge
  • Gregor Miller
  • Sidney Fels
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5815)


This paper introduces a system that automatically classifies registration problems based on the type of registration required. Rather than rely on a single “best” algorithm, the proposed system is made up of a suite of image registration techniques. Image pairs are analyzed according to the types of variation that occur between them, and appropriate algorithms are selected to solve for the alignment. In the case where multiple forms of variation are detected all potentially appropriate algorithms are run, and a normalized cross correlation (NCC) of the results in their respective error spaces is performed to select which alignment is best. In 87% of the test cases the system selected the transform of the expected corresponding algorithm, either through elimination or through NCC, while in the final 13% a better transform (as calculated by NCC) was proposed by one of the other methods. By classifying the type of registration problem and choosing an appropriate method the system significantly improves the flexibility and accuracy of automatic registration techniques.


Image Registration Computational Photography  Panorama Stitching Focal Stacking High-Dynamic Range Imaging Super-Resolution 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Steve Oldridge
    • 1
  • Gregor Miller
    • 1
  • Sidney Fels
    • 1
  1. 1.Electrical and Computer EngineeringUniversity of British Columbia 

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