Full text: Close-range imaging, long-range vision

re reduced as a labeling 
: noise reduction 
"| Noise data 
P| Topographic data 
phic and noise data 
3.2 Interpolation 
There are many lacks of 3D data such as shown in Figure 6, 
then interpolation should be performed by the following 
‘Step (1) 
+ Search a point in neighbouring the removed point. 
+ If detect such a point, search another point in side direction. 
+ If detect these point, the point is judged that belong to the 
same slope, and lacks are interpolated by the point. 
With this process, removed areas were interpolated, but 
diagonally direction is not yet interpolated. Therefore, 
following process is added. 
'Step (2) 
+ Search a point which was interpolated. 
* If detect such a point, search another interpolated point to 
diagonally direction. 
+ If removed points were found between the points, the lack 
space was interpolated using the detected points. 
Here lack of 3D data was interpolated. 
3.3 Unification of Coordinate System 
In generally, many lack of data which is caused by blind spot 
are estimated from a traverse station. Then, measurements from 
multiple stations for the same scene become necessary. 
When laser scanning sensor measure the mirror seal, the seal 
can be found easily in intensity image since the intensity for a 
seal have high brightness. So, mirror seals were set around the 
sites as markers, and coordinates system for multiple traverse 
points were unified automatically as the following procedures. 
+ Set mirror seals at around sites. 
+ Extraction of mirror seal (3D coordinate) in each intensity 
* Generation of TIN models using 3D data of the mirror at 
every stations. 
* Comparison with TIN models between the stations. 
*Matching conjugate mirrors using area, corner angles and side 
length for triangulations. 
*Unification of coordinates system using conjugates mirrors. 
Table 1 shows the equations which were used to unify the 
coordinates system. This expression is generally used for three 
dimensional transformation. 
Table 1 Conversion Expressions 
Coordinate Expressions 
X x'—((x-x,) *cos 0 -(y-yj) *sin Q ) *x,, 
Y y'-((x-xy) "sin  * (y-yy) *cos 0 ) +Yım 
Z z'=(z-z;) +z, 
(x,y,z) = Coordinate before conversion, 
(x,y,z) - Coordinate after conversion 
(x»ys) = Origin Coordinate at before conversion 
(XmYınZm) = Origin Coordinate at after conversion 
0 = Rotation angle 
Figure 6 shows the image which noises were reduced for the 
Figure 3. It can be seen that effective noise reduction were 
performed by the above mentioned methods, similarly it can be 
recognized that the necessity of interpolation. Figure 7 shows 
interpolated image. From this figure, it can be said that the 
interpolation was performed successfully. 
Figure 6. Result of noise reduction 
Figure 7. Interpolated image 

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