Full text: Technical Commission VII (B7)

r orientation 
planning and 
pilot control 
GmbH. High 
during flight, 
are obtained 
ocessing, the 
solution still 
video image 
oint tracking 
acking result 
nate on high 
matching of 
; of this sub- 
ass points. In 
r orientation 
The result of 
of candidate 
jn and scale. 
e positioning 
itrol points is 
st regression. 
ed based on 
delling from 
ure point and 
Code” (OC) 
f OC image 
nstead of the 
ization of the 
st pixel. The 
a ed 
tan AR 
y ; 
Al |+|A | > (1) 
se if |ar|+|ar,|27 
Cy = 
2x ; 
=== otherwise 
In this equation, 4/, and AZ, show horizontal and vertical 
gradient of pixel (x, y). N is the quantization level of direction. 
N is set to 16 typically (Figure 3). The “y” is the threshold value 
for the suppressing of small gradient pixel. 
s Ford i 3x 
Figure 3. Orientation Code (N = 16) 
In order to extract common feature points from OC image, we 
use Orientation Code Richness (OCR) that extracts the pixels 
which have high entropy of OC. The entropy of the local 
limited area of the pixel size M-by-M region at the interest pixel 
(x, y) is calculated as follows, 
P.) die /M*—h,(N) i 
E, =) P,(i)log, P,(i) 
Where h,,(i) (i =0,1,,,,,,N-1) means frequency of OC of M-by-M 
pixel size region. 
When each OC goes with uniform distribution P,,(i)- 1/N, the 
maximum value of entropy £,,. is log;N. Consequently, the 
richness R,, is defined as 
E um if E,2«,E 3 
pay o ob m IS E 
R,, = E x = OE, x y e" max ( ) 
0 otherwise 
Where, the threshold value a, is defined to remove low entropy 
The matching process using OC image (OCM) is similar to 
other simple image based template matching. The difference 
between a template image patch from OC image (Ot) and search 
OC image (Oi) is defined as following equation, 
=-L 40,0) 
M M 
Zo) min{a—&|, N —|a— 4} if a#N,b#zN (4) 
N/4 otherwise 
where D - difference between O, and Oj 
M = Size of template image patch 
d = difference between a pixel and b pixel 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
Figure 4 shows the example of OC image. From this image, it is 
understood that the OC image is independent from the changing 
brightness and also OC image describe the important feature of 
image. Figure 5 shows the result of OCR. The bright pixel on 
OCR means the high entropy of OC. Figure 6 shows the 
example of OCM. From the robustness of OCM, almost points 
are not influenced by the changing of brightness or noise of 
8 E 
a)Original Image (8bit Gray Scale) 
b) OC Image 
Figure 4. Example of OC image 
Figure 5. Example of OCR

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