(c) Matching Strategy: 
* One way Search / Compare and Select from 
Target Window to Search Window 
Two ways (mutually) Search / Compare and 
Select between each other 
xxx 
*** Solve / Determine the Unknowns 
* Multi-Pixel Matching ---» one point 
determination 
*** Multi-Pixel Matching---» Multi-points 
determination ( Weighting the good / high 
contrast pixels to help the poor pixels 
result in higher Reliability ) 
The Minimum Cost Sequence of Error 
Transformation: 
* City Block Distance (Min. Absolute 
Difference) E 
The Min. Euclidean Distance in Euclidean 
Space(Isotropic) (The Weighted Min. Distance 
in Feature Space) ; 
The Least Squares Adjustment (Zvv --» Min.) 
with unequal weight 
xx 
*k*k 
  
  
  
  
  
  
  
  
  
  
  
  
  
4.2 Summary of the process stages and their 
purposes 
Approach Purpose 
[1] Preprocessing *Avoid: 
Earth curvature, 
*Coordinate System Double Precision 
Transformation from Map System *Avoid Matching 
L———————9| to Local Tangent Plane System Failure in 
*Region Matching/DEM Generation homogeneous area 
] 
[2] Tie Point Selection/ *Apply Knowledge 
GCP Identification (Map/Photo) Engineering to 
increase the 
On [2a].Assessment for Good Image degree of 
Board [2b].Structure Feature Detection Automation 
Data Structure Feature Representation 
Structure Feature Matching 
Orbit | *Offer 
Data [3] Aerial Triangulation Orbit Parameters 
/ Tie Point Transfer with Min. No. of 
GCP and best 
distribution of 
GCP. 
*Improve the 
Accuracy of 
Point Transfer 
then improve the 
Accuracy of A.T. 
  
[3a].Bundle Adjustment with On- 
Board Data as the Constrain 
[3b].Using Object Space Least 
Squares Match with Orientation 
Parameter of Scanner as unknown 
to perform the highest accuracy 
Point Transfer interactively 
  
  
  
with A.T. by iteration also. 
] 
[4] Coarse DEM Generation by 
Feature Matching 
*.Correspondence Analysis: : 
*Automatic 
.Image Smoothing 
.Image Rectification 
.Linear Feature Detection 
.Property List Formation 
.String Matching for Linear 
Feature Extraction 
*.Space Intersection for DEM 
Determination 
] 
[5]Refinement of Coarse DEM Data 
Generation of 
Coarse DEM Data 
with 
High Reliability 
ooaus 
 — 9 
  
  
*High quality 
DEM Generation 
with 
subpixel Accuracy 
  
  
Less 
*.Object Space Least Square 
Matching 
  
  
  
5.  CONCLUDING REMARKS 
In general, computer stereo vision belongs to the 
class of Ill-Posed inverse problem. Although the 
Object Space Least Squares Matching is the most 
rigorous method with high precision from a theor- 
etical viewpoint, but it is a serious Ill-Posed 
Problem, and is difficult to implement in prac- 
tice, therefore, every method to improve the 
computational stability of image matching is very 
important, e.g. try to include all available 
geometrical constrains, such as the regularization 
method to minimize the surface curvature is tried 
to help the convergency of solution; the idea of 
pyramidal approach is used to improve the problem 
that the range of convergency is very small, etc.. 
On-Line system for automatic DEM generation can be 
used in the map production line because an oper- 
ator is still involved. It means that all the 
matching algorithms still can not completely solve 
all problems because the images of terrain and the 
terrain itself are so complicated. However, when 
matching has failed, a well trained operator who 
has sufficient knowledge about problem solving of 
137 
stereo compilation of mapping can intervene. 
Therefore, if we can establish a Knowledge Base in 
which there is knowledge such as the well trained 
operator has, and incorporate it into the off-line 
system as an Expert system, we may obtain a simi- 
lar capability as. the on-line system with an 
operator. Hereby, we need to implement our 
approach to the computer in a semi-automatic / 
interactive mode with human interference in order 
to gain more experience and enough knowledge to 
enter into a Knowledge Base; then the problems of 
automatic DEM generation can be solved by an 
Expert System with sufficient knowledge in its 
Knowledge Base. Some primary experiments have 
been done for system start up/ interior orienta- 
tion/ relative orientation in the measurement 
stage to perform diagnostics during the process 
[Kretsch, 1988]; there is still a long way to go 
for handling the whole system by a mature Expert 
System. Therefore, we have to start implementing 
our approach in the computer to accumulate our 
knowledge and improve the Expert System. 
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