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International Archives of the Photogrammetry, Remote Sensin 
automated procedure. Examples of it being carried out 
are abundant in the literature, examples are given by 
Whiteman ef. aL (2002) & Li and King (2002). 
However, this paper is concerned not so much with the 
finding of targets as with the measurement of surfaces, 
defined by numerous points found through the automated 
matching. Although this can often be carried out using 
the same work-stations intended primarily for aerial 
photographic use, the peculiarities of non-topographic 
measurement often mean that it is carried out using 
cheaper proprietary software loaded onto personal 
computers or work-stations or software developed in- 
house such purposes. 
One of the most important aspects of carrying out 
automated measurement on either aerial or non-aerial 
imagery is the "digital image matching", or “image 
correlation" procedure. Over the last few decades, a 
number of approaches have been developed but generally 
these techniques can be classified into two main groups, 
feature-based and area-based matching. The former 
group is fast and reliable and capable of finding matches 
with poor initial values, while the latter approaches have 
the advantage of high precision. It is the latter group 
which is of interest here. 
Area-based stereo image matching techniques make use 
of two small areas (windows) surrounding the point for 
which matching is needed within each image. A 
correlation technique, generally based on least squares 
estimation, selects the point of best match. Methods in 
area-based matching have developed since early 
significant, seminal work by Foerstner (1982) and Grün 
(1985). Further significant extensions of area-based 
matching were proposed by Grün & Baltsavias (1987); 
Rosenholm (1987a & 1987b) proposed a method of 
multi-point area-based matching technique in evaluating 
three-dimensional models, and area-based method was 
further extended by Baltsavias (1991) through the use of 
images from several viewpoints, i.c. with multiple 
images. Further developments of the area-based method 
was proposed by Wrobel (1991) and Heipke (1992) in 
which the matching integrates image matching and 
object surface reconstruction. Recent developments, 
among others, was proposed by Di Stefano et.al. (2002) 
whereby a fast area-based stereo matching algorithm was 
proposed. 
3. AREA-BASED IMAGE MATCHING MODEL 
USING A SURFACE MODEL 
Mathematical details of conventional area-based 
matching (ABM) are provided in many publications and 
are not given here. It may be sufficient to point out that 
ABM is based on least squares solution of a number of 
similar equations, one being written for each pixel in the 
selected “window” surrounding the point to be matched 
on one image. The equation for any pixel incorporates 
the image co-ordinates in one window and the image co- 
ordinates of corresponding pints in the other window, via 
a transformation equation. Conventionally, an affine 
transformation between the windows is adopted, 
(Foerstner, 1982). The unknowns to be determined in the 
least squares solution are the parameters defining this 
transformation - including parameters which define the 
g and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
659 
relative positions of the windows. In effect, these 
parameters represent what are conventionally called x 
and y parallaxes. The equations often also incorporate 
radiometric values but these are not significant to this 
brief explanation. No information of the object is taken 
into account in the matching process. Matching is solely 
based on the intensity values of the pixels and the 
assumed affine transformation. 
An attempt has been made to improve the accuracy of the 
traditional area-based technique as offered by Grün 
(1985). The revision involves replacing the conventional 
model which is used to transform one window shape to 
the other to improve the mathematical description of the 
relationship between the windows in the least squares fit. 
The new transformation incorporates a simple model of 
the surface being measured, and replaces the assumption 
that the windows differ according to an affine 
transformation. lt serves as a compromise between the 
traditional and the far more complex global area-based 
matching method. As with the conventional solution, the 
method proposed here solves, through an iterative least 
squares solution, for the corrections to image co- 
ordinates (x,y) of the search window. But, in addition, 
two ‘new’ unknowns, the gradients in X and Y directions 
on the surface at the point on the surface which 
corresponds to the centre of the search window and their 
second derivatives are introduced. 
Since the transformation used is more rigorous than the 
affine, it is hypothesised that the improved functional 
model will allow the use of larger windows for matching 
and hence improve accuracy. It is also found that the use 
of a better functional model will converge more quickly 
to give a solution. 
The mathematics of the refinement can be obtained from 
another more detailed discussion, (Mustaffar & Mitchell, 
2001). It may be sufficient to point out that the model 
described above is extended by taking into consideration 
the shape of the object. The equations used to define the 
transformation now includes some information about the 
object’s surface which needs an additional significant 
complication of using the known relative positions of the 
cameras. 
4. EXPERIMENTS 
Tests of the algorithms were carried out in the study of 
steel deformations under static loading, see Figure. 
Images were taken using a pair of Kodak digital still 
cameras which comprise of a DC290 and DX4900. The 
DC290 is fitted with a 6.0 mm lens and has a resolution 
of 3.1 megapixles. The lens and resolution for the 
DX4900 are 7.3 mm and 4.0 megapixels respectively. 
These cameras are mounted with a base distance of 
approximately 750mm and the relative orientations of 
these cameras has been determined. A projector was 
placed in between the cameras for the purpose of 
projecting patterns onto the object. The pattern used in 
this investigation was a diamond shaped mesh.