144 
ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 
Fig.3 Distribution Fig.4 Fiducials (a) 
(b) 
(c) 
(d) Fig.5 Similar Object 
Table 3 the candidates of each fiducial mark 
Fiducial 
X (pixel) 
Y (pixel) 
Correlation 
coefficient 
1 
1 
2935 
205 
0.47895 
2 
2935 
169 
0.42382 
3 
2886 
205 
0.41220 
2 
4 
67 
1598 
0.55935 
5 
68 
1655, 
0.43236 
6 
67 
1550 
0.41243 
3 
7 
2965 
4453 
1.00000 
8 
2965 
4420 
0.69096 
9 
2965 
4508 
0.69037 
4 
10 
5817 
1560 
0.28147 
11 
5817 
1609 
0.22221 
12 
5918 
1582 
0.21994 
Fig.4(a) is a good image of fiducial mark. Now one object similar 
to the fiducial mark is added to generate the image in Fig.5. The 
new image is used to test the algorithm. The candidates of 
fiducial mark 3 are listed in Table 5. The point number of added 
object is 8, whose correlation coefficient is also large. The 
parameters of the algorithm are set the same as before. After 
T<T ( „ the correspondence matrix is got as listed in Table 6. A 
correct one-to-one correspondence is got. 
Table 5 the candidates of the third fiducial mark 
Fiducial 
X (pixel) 
Y (pixel) 
Correlation 
coefficient 
3 
7 
2965 
4453 
0.99911 
8 
2913 
4408 
0.99513 
9 
2965 
4408 
0.70765 
4 CONCLUSION 
The interior orientation becomes much harder when fiducial 
marks merge in image of objects. The self-adaptive algorithm 
performs quite well without the usage of correlation coefficient. 
ACKNOWLEDGE 
This paper is supported by the National Natural Science 
Foundation of China (Grand number: 40023004, 40001018) 
Table 4 the correspondence matrix of real image experiment 
m ai 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
Outlier 
1 
0.463 
0.299 
0.237 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
2 
0.000 
0.000 
0.000 
0.482 
0.058 
0.460 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
3 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.499 
0.316 
0.185 
0.000 
0.000 
0.000 
0.000 
4 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.551 
0.435 
0.014 
0.000 
Outlier 
0.537 
0.071 
0.763 
0.518 
0.942 
0.540 
0.501 
0.684 
0.815 
0.499 
0.565 
0.986 
Table 6 the correspondence matrix of the experiment with outlier 
m a \ 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
Outlier 
1 
0.376 
0.298 
0.326 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
2 
0.000 
0.000 
0.000 
0.408 
0.184 
0.407 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
3 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.393 
0.242 
0.365 
0.000 
0.000 
0.000 
0.000 
4 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.000 
0.502 
0.372 
0.126 
0.000 
Outlier 
0.624 
0.702 
0.674 
0.592 
0.816 
0.593 
0.607 
0.758 
0.635 
0.498 
0.623 
0.874 
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