570 
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
The walls were scanned on a cloudy day to ensure as much 
diffused illumination as possible. However as shown in Figure 4 
this was not entirely successful. Walls 1 and 2 contain shadows 
thrown by a nearby tree and a nearby pillar respectively. 
5.1 Walll 
The mortar channel in Wall 1 is about 10 mm deep. Each brick 
is 220 mm wide and 70 mm high. At an average point spacing 
of 3 mm this yields about 2000 points per brick. Therefore, 
invalid bricks, i.e., incorrectly segmented bricks should have a 
point count considerably less than this. A value of 750 (slightly 
less than half a brick) was used in the tests. The rgb triplets of 
each point was first converted to hsv, see Figure 5. The 
following point attributes were used, Hue, Saturation, Value, 
intensity and mean distance to neighbouring points. 
Figure 5 Point hue (left), saturation (middle) and values (right) 
of wall 1. The value component suffers the most from the 
shadow (dark area) cast by the tree. 
The edge strength was computed as the product of the point 
attribute at both edges or the length of the edge. Attribute 
difference was also tried, but the attribute product provided the 
best discrimination between brick and wall points. Once the 
edge strengths had been set the edges with a value below a 
given threshold were removed. The best value for the threshold 
was obtained from the mode seeking technique described in 
section 4.7. 
As shown in Figure 6 the segmentation is most successful when 
edge length is used as the edge strength criteria. This is helped 
by the fact that the mortar channel is both deep and wide and 
the scan resolution is fairly high. The results of the hsv 
segmentation are fairly similar with the value segmentation 
possibly performing worse than the hue and saturation 
segmentations. The hsv segmentations yield at least 5 over 
segmentations on the left side of the wall. This is because the 
segmentations were optimized for the right side of the wall. 
In Figure 7 is shown the means by which the optimum threshold 
is selected. In this example invalid segments have a point count 
of 750 or less, and the edge strength is based on edge length. 
The images show the segmentations at different points on the 
graph. Note that as expected the optimum result occurs near the 
peak of the curve. 
5.2 Wall 2 
Wall 2 presents a more difficult problem and is typical of the 
brick detection problem trying to be solved. The mortar channel 
is both shall and narrow. Moreover the granite bricks are of 
different shapes and sizes. Here invalid bricks were chosen as 
containing a 750 points or less. The rgb triplets of each point 
was first converted to hsv, see Figure 8. 
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Number of components vs. Edge strength threshold 
140 
Edge strength threshold 
Figure 7 Selection of threshold for segmentation.