ul 2004 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV , Part B-YF. Istanbul 2004 
  
Table 2. Results of line matching 
      
  
     
  
. 09 09000 0000000 9 990 90 09 9 000.00 000 
   
  
  
  
  
  
  
  
  
00000000 
  
  
  
...000.000 
      
(a) Proposal method 
(b) LSM method 
(c) Probabilistic relaxation method 
(d) Area correlation method 
  
(a) LSM 
| were 
1e line 
which 
s. The 
hese 3 
are fj; 
hese 3 
lowing 
  
Figure 4. Result of line matching by proposal method 
6. EPIPOLAR MATCHING 
(4) The line matching was performed efficiently by the above 
procedures. However, these procedures can not apply for all 
necessary lines due to fragment or multiple. Therefore, the 
unmatched lines were corrected using epipolar matching. 
The epipolar matching was performed using epipolar lines for 
the first and last image. In order to estimate epipolar lines, 
relative orientation was performed by coplanarity condition 
using the first and last image. The both ends for the each 
matched lines were used as pass points, and the orientation 
int of parameters (9j, Kj, €», 9», K;) were determined. After the 
orientation, geometric correction of the first and last image was 
  
(b) Probabilistic relaxation 
  
  
  
  
  
  
  
  
  
  
  
en performed using the orientation parameters. Consequently, 
ded to epipolar lines were estimated. 
of the Furthermore, in order to perform stereo matching using these 
e third epipolar lines efficiently, stereo matching was performed by 
above 3 general methods, and performances of each method 
were compared. As a result, LSM method realized efficient 
stereo matching more than other 2 methods which shown in 
Table 3 and Figure 5. Consequently, LSM method was adopted 
tching for the epipolar matching in this paper. (c) Area correlation 
. gH Figure 5. Results of epipolar matching 
igated, | Table 3. Results of epipolar matching 
] with | 7. 3D MODELLING 
ichine | 200000 | 020 2002020000000 | 0 o 000 cou acc eu 0se 
could | iin idis emos dtes The line information for 3D modelling can be acquired 
thods. Sect e eee Sas efficiently by the method in the previous chapter. However, 
tensor | ao Sa maar its ure each rooftop of building in the urban area is needed to be 
Figure | (a) LSM method recognized for 3D modelling. Therefore, rooftops recognition 
| (b) Probabilistic relaxation method was performed by morphological opening procedure, and the 
| (c) Area correlation method extracted rooftops were conjugated with the matched lines in 
this paper (Kunii and Chikatsu, 2003). Figure 6 shows the result 
of the opening procedure for the first image. 
| 223