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Juliang Shao 
  
to exemplify the multi-image matching via segment features. This comprised a three-image aerial coverage of part of a 
town in Europe, as shown with the initially extracted line segments in Fig. 9. At full resolution, there existed a 
discrepancy in image scale of close to 2096 between the Image a and the other two involved. There was also a rotation 
of 25^ between Image c and Images a and 5. The segment matches displayed in Fig. 10 were derived in the initial 
matching approach applied to the three images. The matched segments identified at the conclusion of the relaxation 
process, but prior to extending the initial matches, are shown in Fig. 11. The final outcome obtained via extending the 
initial matches and applying the relaxation processing is displayed in Fig. 12. The final refinement led to a significant, 
14.7% increase in the number of valid segment matches, whereas the rejection rate from the initial matching was 
27.7%. This highlights the benefits to be gained by application of a relaxation processing through local consistency 
checks. The results also indicate that the affine model used was well suited to the prediction of matching candidates. It 
should be noted that for segment correspondence, one reference segment matching to multiple candidates is reasonable 
when multiple candidates coincide with the same line and each has a common part with the reference segment. The 
common part stands for the overlapped part restricted by epipolar geometry. 
  
  
  
Fig. 11: The results of the relaxation processing to the non-extending matches. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000. 843