The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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calculation steps, the network generates binary images, with 
value 1 pixels corresponding to the emergence of a pulse, and 
value 0 signals corresponding to the lack of a pulse. 
In research works described in Chapter 4, that network was 
used as a generator of an aerial image fragment representation. 
That representation is the so-called signature. It is a single 
dimensional function, created after the application of the (4) 
formula to subsequent binary images generated by the network: 
G W=Z y «W (4 > 
Uj 
with n range of changes determining the length and location of 
the signature. 
3.2 Biological neural network model 
Another type of image representation can be obtained after the 
image has been processed with log-polar and log-Hough 
transformations. Those transformations can be classified as 
belonging to the broadly-understood class of neural networks, 
as they are simplified models of visual information processing 
that takes place on retina (Schwartz, 1977; Weiman, 1989). 
The principle of operation of a log-polar transform is shown in 
Fig. 2. A grid of concentric circles with exponentially 
increasing radiuses is put on an image. Then, through a uniform 
division of a round angle, the so-created rings are divided into 
receptor fields. Information in receptor fields is averaged, so the 
log-polar representation is the most precise when close to the 
image centre (near the so-called dead zone, that is a non- 
transformed area), and less precise when close to receptor fields 
located far from the centre. Main advantages of the log-polar 
transformation include a considerable reduction of data, as well 
as transformation of image rotation and magnification in the 
Cartesian space to parallel shifts along the respective axes in the 
log-polar space. 
Using the log-Hough transform, it is possible to detect sections 
of straight lines and gentle arcs on log-polar edge images 
(Weiman, 1989). In the log-Hough space, there appear maxima 
in places that correspond to the parameters of detected lines. 
Those maxima are the higher, the more collinear points were 
found in the image in the log-polar space after the separation of 
edges. 
receptor 
field 
blind spot 
fragments of lines with different inclinations. Both those 
transforms were selected to design the image representation, 
because they reduce information and provide condensed 
information on the location of edges in relation one to another. 
4. SCOPE OF RESEARCH 
The conception of a large-scale research on the use of neural 
networks in photogrammetry is shown in Fig. 3. Bearing in 
mind the division of image processing techniques into those 
"object-centred" and "area-centred" ones, the authors focused 
on the latter ones. 
sub-image 
Figure 3. Flow chart of the experiments. Neural-like methods 
are denoted in italics 
The left fragment of Fig. 3 includes two methods that are based 
on the utilization of gradient images. When analysing the right 
part of the drawing, one should note that the “raw” image 
constitutes an input both to log-polar transform and ICM 
network. 
Fig. 3 also shows, at the bottom part, the goals set by the 
research team. The first of them included the creation of aerial 
photo sub-image representations, which were later used for 
selection (the second goal) and, at the same time, for matching 
(the third goal). It is that second goal that requires a separate 
discussion here. 
Figure 2. Distribution of receptor fields in the log-polar 
transformation 
In the course of a photogrammetric analysis, one indicates 
informative points on one of the images, seeks out first a larger 
area on the second image, and then equivalents of points 
indicated on the first image. The second goal (selection) 
The operation performed by the log-Hough transform is similar 
to the transformation observed in the visual cortex: detecting