International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
  
  
  
  
  
  
  
Figure 1 An image of the steel beam tested 
The images used are shown in Figure 1, where the mesh 
projected is apparent. Points of interest in the image are 
the intersections of the mesh. The selection and pairing 
of corresponding points for precise matching was carried 
out manually but techniques for finding such points 
automatically can be achieved using epipolar approach. 
Two hunderd pairs of points were selected for matching 
and as part of the experiment, the size of the window 
chosen was 25 x 25pixels. 
5. SUMMARY OF RESULTS 
Results obtained from the proposed method, were then 
used in a space intersection routine in order to obtain the 
shape of the deformed steel. Figure 2a and 2b show the 
contour and shape of the deformed surface obtained 
respectively. Closer inspection revealed that the 
difference between the highest point and the lowest point 
on the surface is 7.5 mm. 
  
  
s: > 
860 880 900 920 940 960 980 1000 1020 1040 
Figure 2a. Contour plot of thedeformed steel surface 
The internal precision, which is indicated by error 
ellipses computed using the standard errors obtained 
from the least squares adjustment is found to be good. 
For example, at window size 25 x 25, the size of the 
semi-major axis of the error ellipse is 0.01 pixels. 
6. DISCUSSION AND CONCLUSIONS 
This experiment has shown that the ABM using surface 
models can be successfully applied to the measurements 
of surface deformations of steel structures. Nevertheless, 
as the results presented are those obtained under 
laboratory conditions, further tests are needed to arrive to 
a more thorough conclusion. 
660 
It is intended that the method employed serves as an 
avenue for structural engineers to obtain additional 
information in their analysis of steel structures. 
  
Figure 2b. The three-dimensional surface plot of 
the deformed steel surface 
REFERENCES 
Baltsavias, E.P., 1991. Multiphoto Geometrically 
Constrained Matching. Ph.D. thesis, Institute of Geodesy 
and Photogrammetry. Mitteilungen Nr. 49, 221pp. 
Di Stefano, L., Marchionni, M., Mattoccia, S. & Neri, G., 
2002. A Fast Area-Based Stereo Matching Algorithm. 
Foerstner, W., 1982 . On the Geometric Precision of 
Digital Correlation. Proceedings of ISPRS Commission 
III Symposium, Rovaneimi, Finland, June 7 - 11, 1982. 
In IAP 24(3), pp. 176-189. 
Grün, A.W., 1985. Adaptive Least Squares Correlation : 
A Powerful Image Matching Technique. South African 
Journal of Photogrammetry, Remote Sensing and 
Cartography. 14(3), pp. 175-187. 
Grün, A.W. & Baltsavias, E.P., 1987. High Precision 
Image Matching for Digital Terrain Model Generation. 
Photogrammetria 42(3), pp. 97-102. 
Heipke, C., 1992. A Global Approach for Least-Squares 
[mage Matching and Surface Reconstruction in Object 
Space. Photogrammetry Engineering and Remote 
Sensing, 58(3), pp. 317-323. 
Mustaffar, M. & Mitchell, H.L., 2001. Improving area- 
based matching by using surface gradients in the pixel 
co-ordinate transformation. ISPRS Journal of Photog. & 
Rem. Sensing, 56(1), pp 42-52. 
Pertl, A., 1985. Digital Image Correlation with an 
Analytical Plotter. Photogrammetria, 40, pp. 9-19. 
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