that the second-best grid cells matching enhances the dense of 
initial matching results, more abundantly expresses the object 
details, and gets the homologous points in two overlapping area 
and occlusion area in the multi-view image. 
  
  
  
  
  
Figure 8. Grid cells of final matching results 
3.3 Result evaluation 
This paper makes forward intersection with the homologous 
points, and obtains the discrete 3D points (Fig. 9). From this 
figure it can be seen that there are a few mistakes. As shown in 
Fig. 9, the ellipse regions includes the three error matching 
points obtained in initially matching of the best grid cells, and 
the seven error matching points obtained in matching with the 
second-best grid cells. So it needs to adopt a reliable filter to 
reject the mismatching results. 
  
Figure 9. Discrete 3D points 
3.4 Contrast analysis with different constraint conditions 
The existing multi-view image matching object constraint 
models, such as GC? algorithm (Zhang, 2005), modified vertical 
line locus algorithm (MVLL) (Ji, 2008), constrain the image 
space search range by points to be matched moving in the range 
of an approximate height value along a certain linear direction 
(projection ray locus, vertical line locus) in the object space, 
and reduces the search range from two-dimensional to one- 
dimensional. The traditional standard normalized cross- 
correlation (NCC) is expressed as a function of height value Z, 
in the approximate height value ranges 
Z e[Z, — AZ, Z, + AZ]. The traditional algorithm calculates 
the sum of NCC (SNCC) values iteratively, and selects the 
height value Z corresponding to the maximum SNCC value as 
the object correct height value. The formulas are, 
2,0. G) - 4), G,G) - 1) (4 
NCC;(p,,Z)- ET 
(2,00 zh 12a SAL) 
seW seW 
  
   
1 n 
SNCC(p,,Z) =~ NCC,(p,. Z) 65) 
noa 
Taking the point P in the image Ll as an example, and 
according to the geometric constraint principle in GC? algorithm, 
this paper restricts the height search range to 3-10m, and the 
search step is 0.1m. The calculated cross-correlation curve is 
shown as Fig. 10. The final calculated result shows that the 
SNCC < 0.6 and obtains the maximum at the point p which 
should be the point P . The reasons include (1) points to be 
matched in the image LI are occluded in image L3, and the 
NCC, 2 < 0.42 which is too low and (2) the NCC 
L1-L2 at 
point D and point p' appear peak values because of the 
repetitive texture in image L2, which means the local grey 
distribution at point P and point p' is similar. 
Fig. 11 is the matching result using the MZPC algorithm. In the 
matching of the second-best grid cells, it matches the grid cells 
with number = 2 on the plane Z 2 7, and selects image L1 
and image L2 according to the projection rays passing through 
the grid cells, and avoids the occlusion in image L3. The ANCC 
value is 0.94825602, and the correct matching result is obtained. 
Comparing with the existing multi-view image matching 
algorithm based on the object constraints, the MZPC algorithm 
limits search distance in the image space under traditional 
epipolar constraints or the search distance in object space under 
geometric constraints to a grid cell position in the plane, which 
avoids the appearance of multiple peaks in the cross-correlation 
curve caused by similar texture, and reduces the probability of 
mismatches. Simultaneously, the MZPC algorithm carries on 
the selective matching for multiple images according to 
projection rays in the grid cells to avoid the effects of 
occlusions, and thereby improves the reliability of the matching. 
       
n3 OF o: eet X y (ir 
s NCC L142 : d R SNCC i W & NCC Li 
rt ; “Ÿ 
13456789591) 324586785i0 324538675910 
  
     
      
Figure 10. Experiment results by projection rays constraint 
matching 
  
L1 | L2 
Figure 11. Experiment results under moving Z-Plane constraint 
  
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