TROLOGY 
nnes@avt.at 
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ated VM process 
] targets in each 
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th the discussion 
only. The strategy 
d target quality 
ncluding distinct 
e lc shows that 
n the targets and 
their background, all have a common feature, namely distinct 
edges. Figure 2 shows the respective intensity profiles through 
these targets. Although the distinct jump in grey values at the 
edge of the targets varies in magnitude, the edge gradient in 
each case is more or less the same (recall that we design targets 
to yield such edge gradients). Thus, the gradient is generally a 
better criterion for the detection of targets than the commonly 
adopted simple pixel-value thresholding (e.g. Atkinson, 1996; 
Luhmann, 2000). 
  
Figure 1. Target images in VM: high-quality, near-binary (a), 
low-quality (b) and a range of images (c). 
The grey value gradient is expressed via the first derivative of 
the intensities. With the image being interpreted as a two- 
dimensional discrete function, the first derivatives in row (x) 
and column (y) direction can be given as 
"x t 
8;,;78,j178i;j 
"P i 
fi Sins Si 
(1) 
In the literature, alternative operators are also described (e.g. 
Gonzalez & Woods, 1992), with some using more than two 
neighbouring pixels to compute the gradient at a certain 
position. However, derivatives using only two adjacent pixel 
values are well suited to near-circular targets, and they have the 
advantage of simple and fast computation. Performance 
30 30 
20 az 
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Lu m 
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considerations are very important for VM scanning algorithms, 
with higher contrast imagery generally leading to optimal 
computational speed. 
2.2 Image Segmentation 
Given the edge characteristics discussed, a line-orientated image 
segmentation algorithm that uses the gradient as a target 
recognition criterion would appear as a good candidate for VM 
applications. The gradient criterion alone, however, can deliver 
unsatisfactory image segmentation results in conjunction with 
low-quality images, since brighter areas are always recognised 
as targets if they are surrounded by darker regions. To alleviate 
this problem an extended segmentation algorithm has been 
developed for Australis, which uses both the gradient criterion 
and a minimum grey value threshold to detect target regions. 
In comparison to algorithms that only use minimum grey value 
thresholding, the value of the adopted minimum threshold in the 
approach described is quite low. The threshold is not used so 
much to differentiate between isolated bright areas and targets. 
Instead, it only guarantees that pixels below a certain grey 
value, which are definitely non-target pixels, are rejected from 
further target measurement processes. The two thresholds used 
in the scanning process, the gradient threshold and the grey 
value threshold, need to be carefully chosen to ensure 
satisfactory results in the segmentation process. A fixed 
gradient threshold is generally appropriate for quasi-binary 
images as well as for low-quality images. However, the grey 
value threshold has to be separately selected for each image 
using histogram analysis. 
With this development, the line-orientated scanning algorithm 
of Australis is implemented as follows: The process starts at the 
first pixel line of the image and goes through every pixel in that 
line. If the current pixel position fulfills both the grey value and 
gradient threshold criteria, the beginning of a target blob is 
indicated. The end of the target blob is detected either if the 
pixel grey value is below the minimum threshold or the gradient 
threshold is exceeded in the negative direction. If the ‘negative’ 
gradient criterion is detected at multiple positions, the last 
position is taken as the end of the target. It may happen that the 
criterion is met, without the detection of a target beginning. In 
this case, if the actual pixel is above the minimum threshold, the 
start of the target region has obviously been missed. To find the 
beginning of the missed blob, the algorithm searches backwards 
until the pixel grey value drops below the minimum threshold. 
  
Figure 2. Intensity profiles through the targets of Figure 1c. 
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