The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
3.2 Variable depth and width of the mortar channel 
Typically the mortar between bricks is indented from the 
surface of the bricks. This indentation is called the mortar 
channel. When the mortar channel is deep, brick surfaces easily 
stand out from the surface of the wall. In a laser scan this 
appears as a surface discontinuity between the points on the 
mortar and the points on bricks. If the mortar channel is shallow, 
there is no surface discontinuity between mortar and brick, and 
the segmentation yields segments that contain both mortar and 
brick, i.e., under segmentation. In masonry walls, very often 
bricks are packed tightly against each other and the gaps are 
filled with mortar. Because of this, segmentation can yield 
segments containing more than one brick. 
3.3 Similar surface covering 
Ideally, if bricks and mortar are made of different materials, it 
should be possible to segment a wall based on the rgb values 
(colour) of points. In practice this is not possible if bricks and 
mortar are covered by paint, lichen, moss or a combination. 
This gives bricks and mortar the same appearance or worse a 
patchy appearance. Another variation on this problem is when 
the bricks and mortar are made of the same material. For 
example mud walls. 
The above problem might be overcome by acquiring multi- 
spectral images of a wall. 
3.4 Discontinuities in brick surfaces 
Segmentation assumes that a surface discontinuity only exists 
between the surface of mortar and bricks. Bricks in a masonry 
wall sometimes have surface discontinuities within them. This 
leads to an over segmentation which may then complicate the 
detection of whole bricks. 
3.5 Surface lighting 
Depending on the lighting parts of bricks and mortar may be in 
shadow or highlighted. This complicates the segmentation of a 
wall based on the shading/brightness of objects. Furthermore, it 
complicates segmentation based on rgb, since brightness is 
embedded in the rgb components. Converting rgb values to hsv 
(hue, saturation and value) should help to untangle the effect of 
the variability of surface lighting from the hue. 
Preferably a wall should be scanned under diffused illumination 
as available on a cloudy day. However, this may not always be 
possible. 
3.6 Variable resolution of point cloud 
Choosing the parameters of segmentation assumes that the local 
geometric surface characteristics of brick and mortar surfaces is 
uniform throughout the point cloud. In practice this is not the 
case for the following reasons: 
- The edge of a scan will have a lower resolution than the 
centre of the scan, 
- The overlap area of two or more scans will have a higher 
resolution than other areas of the scan. 
- Objects at different distances from the scanner will have 
different scan resolutions, as shown in 
Figure 1. This applies to walls that are embedded, i.e., 
walls at different distances from the scanner. 
Furthest wall is of a 
lower resolution 
Figure 1 Variable resolution caused by walls at different 
distances from the scanner. 
The above complicate the selection of optimum thresholds for 
segmentation criteria. One possible solution to this problem is 
to apply different thresholds optimised for local point cloud 
resolutions. Alternatively, a single threshold can be used and 
local segmentation criteria can be weighted based on local point 
cloud resolution. 
4. PROPOSED DETECTION ALGORITHM 
The proposed solution is based on weighted proximity 
segmentation. The method works on the assumption that the 
mortar channel is reasonably deep and wide (at least two points). 
The method is designed to work on a 3D point cloud. This is 
necessary because the point cloud of a wall is typically 
composed of more than one scan. The sequence of steps in the 
algorithm is outlined in the following subsections. 
4.1 Pre-processing 
The rgb value of all points is converted to hsv. As mentioned in 
section 3.5 this is done to untangle the effect of surface lighting 
from the hue. 
4.2 Triangulation 
The point cloud is triangulated using a 3D Delaunay 
triangulation, see Figure 2(b). The triangulation is stored in a 
graph G(V, E). The points in the point cloud are given by the 
set V and the edges of the triangles by the set E. The graph is 
used as a data structure to search point neighbourhoods and 
effecting the connected components (explained in section 4.5) 
4.3 Point attributes 
Radiometric and geometric attributes are associated with each 
point. The radiometric attributes include the intensity, hsv 
triplets, and functions of the intensity and hsv of neighboring 
points. Geometric attributes include the statistics of the 
Euclidean distance to the N nearest points, and the normal at a 
point. 
4.4 Edge attributes 
From the point attributes edge attributes are determined. These 
attributes represent the strength of the connection between 
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