International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012 
XXII ISPRS Congress, 25 August - 01 September 2012, Melbourne, Australia 
125 
Test data 2 
Area with high 
buildings. 
Size: 3000*3000 
Test data 3 
The normal area 
Size: 4096*4096 
Figure 5 three test data set. 
The time cost of each processing step is showed in table 1. 
Table 1 processing time 
Processing step 
Spending time (ms) 
Datai 
Data2 
Data3 
1. Wallis filter 
672 
1321 
2321 
2. feature extraction (Harris) 
122 
231 
321 
3. calculate left image vector 
(10*10 points) 
32 
35 
37 
4. calculate right image vector 
(all pixels) 
753 
1424 
2531 
5. find potential correspondences 
12 
12 
11 
6. remove error points by GHT 
11 
11 
12 
Total 
1602 
3034 
5233 
Table 2 processing time by SIFT 
Spend time (ms) 
Datai 
Data2 
Data3 
Total 
42304 
93872 
244387 
(Download code form 
http://www.cs.huji.ac.il/~ofirpele/SiftDist/codc, 
Compiling by Microsoft Visual V++ 6.0 ) 
The matching results are showed in Figure 6. 
2a 2b 
3a 3b 
Figure 6 Matching Result of The Method 
As can be seen from the results, the image matching 
algorithm based on the rotation vector field can indeed solve the 
problem of image rotation , and the computing speed is much 
faster than the traditional template matching algorithm in which 
each pixel is involved in complex operations ( such as 
convolution). 
As we can see, the method can achieve good matching points 
which can be used lately as the initial-value for the accurate 
matching. 
4. CONCLUSION 
According to the above experiments, conclusions can be 
drawn as following. 
Firstly, the matching algorithm based on rotation vector field 
is an optimized method which greatly reduces the computation 
and improves the matching speed. 
Secondly, the algorithm can get good results in regions with 
rich features and few repeated features. And it’s not suitable for 
regions that with poor feature or have a large number of 
repeated features. 
Thirdly, the accuracy of the matching points obtained by this 
method is not that high. And these points can’t directly 
participate in photogrammetry processing but can be used as the 
initial-value for the accurate matching. 
Our further research will focus on the scale-change problem 
in the image matching which hasn’t been solved yet. 
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Herbert Bay,Tinne Tuvtellars and Luc Van Gool. 2006. SURF: 
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Zhang Li,Zhang Zuxun,Zhang Jianqing. 1999.The Image 
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Technical University of Surveying and Mapping, Vol, 24 
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Sim D G, Kim H K, Oh D I. 2000. Translation, scale, and 
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Farhan Ullah, Shunichi Kaneko. 2004. Using orientation codes 
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