Full text: The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

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 
REFERENCES 1 
(1) . Zhang Z., Zhang J.. 1996. Digital Photography. Press of 
wtusm 
(2) . Christian Heipke 1997 .Automation of interior , relative , 
and absolute orientation .ISPRS Journal of 
Photogrammetry & Remote Sensing 52(1997)1-19 
(3) . Kersten T and Haring T.1995 Automatic interior 
orientation Internal report, ETH Zurich 
(4) . Lue,Y, 1995. Fully operational automatic interior orientation 
Proceedings Geolnformatics’95,HongKong,May 
26-28,Vol-1,pp.26-35 
(5) . Schcker.W. 1995. Ein operationeiles Verfahren zur 
automatischen inneren Orientierung von 
luftbildern .Z.Photogramm,fernerkundung,63(3):115-122 
(6) . Strackbein,H.and enze.M.,1995.Automatische innere 
Orientierung.Internal report .Landesvermessungsamt 
Nordrhein Westfalen 
(7) . S.Gold, A. Rangarajan, C. P. Lu, S Pappu, and E. Mjolsness. 
New algorithms for 2-D and 3-D point matching : pose 
estimation and correspondence . Pattern Recognition, 
31 (8): 1019 1031, 1998 
(8) . S.Gold, and A. Rangarajan. A graduated assignment 
algorithm for graph matching. IEEE Trans. Patt. Anal. 
Mach. Intell., 18(4):377-388,1996. 
(9) . Haiti Chui and Anand Rangarajan 2000 A new algorithm for 
non-rigid point matching , IEEE Conference on Computer 
Vision and Pattern Recognition (CVPR), 
(10) . Girosi, F.,Jones,M.,and Poggio,T.(1995). Regularization 
theory and neural net work architectures. Neural 
Computation, 7:219-269 
(11) . Papadimitriou, C. and Rangarajan, K. (1982). 
Combinatorial Optimization. Prentice-Hall,Inc.,Englewwood 
Cliffd.NJ. 
(12) . Gold,S.,Rangarajan,A.(1996). Softmax to softassign: 
neural network algorithms for combinatorial optimizations 
Journal of Artificial Neural Networks, in press
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.