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id 
op BER 
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Timur Murat Celikoyan 
  
42 43 1.1 12 
3.3 A IN. 2.1 
3.2 31 23 22 
Figurel: Numbers of pixel neighbourhoods in the algorithm (Celikoyan and Altan, 1999) 
  
In Fig.l. the first numbers are referring to the circle number, where the seconds to the neighbourhoods. Aiming 
matching the borderline of the detail, grey value of the 1.1" neighbourhood has taken from the array, which belongs to 
the image. If the difference of grey values between standing pixel and the 1.1" neighbour is in the difference border 
given by the user, this pixel is matched. In the other case the same process is done with the pixel located in the 1.2™ 
neighbourhood. This process is used until the target pixel is found. If any targeting pixel cannot be found in a circle, the 
search process goes on with the next circle. Nearby this, if a pixel is found, the next search begins with the 1" neighbour 
of this circle. An important point is that, if a pixel is found with a 1". neighbour of any circle, this pixel is marked using 
the 3™ neighbour of previous circle. In that way, the search process can change its rotation and every borderline from 
multiple geometry can be extracted. This search process stops until any pixel could be marked using all the neighbours. 
Another point is that the data size after running this algorithm is so large that it slows down the computer process speed. 
In order to avoid this problem, only break points of the borderline are selected. For this purpose, gradient of this 
borderline is determined in every point and the points, by which this numerical value has changed, are taken to the data. 
In that way, the size of the data is minimised approx. from 1 MB to 30-40 KB. (Celikoyan and Altan, 1999) 
3 EXTRACTING GEOMETRICALLY CLOSED OBJECTS 
The basic process of extracting geometrically closed objects does not have a big difference from the extracting 
procedure of non-closed objects. The algorithm, mentioned in Caption 2 is adapted so that it stops, when a previously 
marked pixel is reached for one more time. In that way, geometrically closed objects are extracted. The handicap by this 
process is to check the whole image coordinates of previously marked pixel by every search process. This difficulty 
slows down the process proportionally to the running time. The big amount of pixels marked before, the slower running 
speed of the algorithm. To avoid this problem partially, the object type, which will be extracted, is given by the user 
before the search process. In that way, extraction process of non-closed objects does not slow down. 
‘4 APPLICATIONS 
As first application, damaged buildings by Dinar (1995) and Marmara (1999) Earthquakes are given. Results of these 
applications can be used in Geographical Information System developed for modelling and damage analysis of 
buildings. The images are taken by using Kodak DCS 200 digital camera (1524 x 1012). As it seen in Figure 2, cracks 
on the damaged building have different grey values from the undamaged part. Using this difference, damages can be 
extracted from digital images. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000. 165