CAM 1 
  
  
  
  
CAM 2 CAM 4 
  
  
CAM 3 CAM 5 
  
  
  
TARGETS 
SUBJECT 
Figure 10. The position of the camera stations. 
Following the recognition, location, and labelling 
process the coordinates of the targets from each camera 
station were passed to a software package called the 
General Adjustment Program (GAP) developed at City 
University, and the resulting 3-D data were used to 
construct a wire frame view of the turbine blade surface, 
see Figure 11. 
  
Figure 11. A 3-D view of a section of the turbine. 
The research indicates that in some situations where 
there is prior knowledge of the subject to be measured 
automatic target recognition is possible. Furthermore, 
the use of subpixel techniques can enhance the accuracy 
of CCD cameras beyond the nominal accuracy 
suggested by sensor size and numbers of pixel. Hence, 
such image processing hardware and software methods 
may overcome some of the disadvantages of manual 
target identification and location which is both time 
consuming and tedious. 
The investigations described in this paper used an 
essentially simple structure which allowed the use of 
straightforward strategies to achieve good results. 
However, in most cases, more complex structures with 
non-ideal imaging characteristics are required to be 
measured. This research has allowed the authors to 
become acquainted with some photogrammetric 
methodology and has given insight into directions for 
further work. Such work should address four main 
areas: 
    
(i) The target recognition, location and labelling 
method could be seamlessly integrated into the 
bundle adjustment method. 
(ii) The use of transformations based on central 
perspective projection e.g. epipolar, to extend the 
labelling process beyond 2-D object assumptions. 
(iii) The target location algorithms may be further 
refined to give greater accuracy, reliability, or 
efficiency. | 
(iv) The binary method could be dispensed with if 
alternative methods could be devised for use in grey 
scale images. 
Further research which will build upon the 
multi-disciplinary approach is underway. It is hoped 
that a greater understanding of photogrammetric 
methodologies will result in a useful contribution to 
machine vision problems. 
6. ACKNOWLEDGEMENTS 
The authors would like to acknowledge the support of 
a number of colleagues: Prof. K.T.V. Grattan, Dr. S. 
Robson, Dr. T J. Ellis, and the provision of the software 
package "GAP" which was developed in the 
Photogrammetry Unit in the Civil Engineering Dept, 
City University. 
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