pore 
m sat- 
eal 
inges 
| fea- 
from 
em, 
y win- 
'lassi- 
n…al- 
to the 
P ob^ 
j im- 
ispec- 
1eces- 
iture 
3 also 
nputa- 
red 
1d the 
tail, 
Por 
ne to- 
essen- 
ay). 
xel and 
eeds a 
es ad- 
sa 
struc- 
depend 
     
2. Basic Theoretical Concept 
Contrary to the aim of the adjustment computation, it is not 
intended here to determine the best fitting transformation 
funetion between the two feature vectors x and y. A linear 
funetional model is rather hypothetically assumed and the 
residuals, resulting after the adjustment are examined as to 
random or systematic deviations from this best fitting 
straight line. Thus, a conclusion can be drawn to a linear 
or non-linear dependency of two pixels each from both fea- 
ture vectors x and y. The formulae used for this purpose is 
indicated by Wolf [2]. 
In the following, it is con- 
y sidered that the x; and y; 
are of the same accuracy. Ad- 
ditionally we assume not to 
minimize the D sd but the 
sum of the squared vertical 
  
distances [vv] of the meas- 
y 
  
ured points from the best fit- 
Fig.1 ting straight line G (see Fig.1). 
As the adjusted straight line fits the central point S of 
the measured points, one obtains the following observation 
equations: 
Vi = E; Sin 9 — N,°C0S 9 \ d 
; = TE = iz) 
with E1 = Xs eos and "n; = Yi — 
Substituting 
2 = (zn), b=022) = [nnl, ec= [5858] + {nn} and n 
2 
  
V(fzzi - [nniY? t BlEnlt, 
one obtains for the ascent of the straight line 
d 
2