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ging problem in photogrammetry. The human operator does it with great 
ease, and the performance of computer methods will always implicitly be 
compared with that of human operators. 
The problem of stereo matching can easily be formulated as a three- stage 
process: firstly, a pixel location that corresponds to a point of the surface 
of the object is chosen in one of the images; secondly, the corresponding 
(conjugate) pixel in the other image (or in case of multiple coverage in all 
other images) is located; and finally the three- dimensional position of the 
point is determined. Since pixel locations are in fact image coordinates 
the latter step can rely on the well- established methods of analytical 
photogrammetry. 
Traditionally, in photogrammetry the preferred methods for stereo 
matching are those of area correlation. The assumption is that the 
conjugent points can be found by correlating the image function of the two 
images. These methods produce good results in many cases; in others, 
however, they fail. It was always hoped that further refinements of the 
correlation technique would solve the remaining problems. 
During the last ten years, a new theory about the human visual system has 
emerged and computer models have been developed to test the theory 
(see, e.g., /3/,/6/,/7/). One of the interesting conclusions is the fact that 
the human visual system does not find the corresponding points by 
matching raw intensity values (gray levels), rather it matches abrupt 
changes in the image function which often reflect physica! boundaries in 
the object space. When accepting the human visual system as superior to 
any automated system the message becomes plain: the area correlation 
and its many variants is the wrong method. 
Stereomatching in the human visual system 
Vision is our most impressive sense. What we do without conscious effort 
is a massive information process that has not been well understood until 
only recently. "Seeing' seems easy and straightforward so that one 
drastically underestimates the problem. It turns out that for a computer to 
perform the simplest act of vision requires millions of multiplications. 
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