UNIFORMITY AND PROXIMITY APPLIED TO THE GENERALISATION OF TARGET FIELDS 
IN CLOSE RANGE PHOTOGRAMMETRY. 
L. C. Anderson (Masters Candidate) and C.J. Bellman (Lecturer) 
RMIT University. 
Australia. 
Commission V, Working Group 1. 
KEYWORDS: Design , Close range, Networks, Expert system, Automation. 
ABSTRACT: 
The design of close range photogrammetric networks can be a difficult task requiring a good understanding of the factors 
which influence network design and accuracy. The configuration of the network geometry is a critical factor in determining 
the accuracy which can be achieved for a survey. Expert photogrammetrists draw heavily upon heuristic knowledge and 
experience throughout this design process. The expert knowledge required has been identified as one of the limiting factors 
in the application of close range photogrammetric techniques (Mason, 1994). Expert systems offer a means of automating 
the network design process. Mason (1994) proposed a conceptual framework for network design using an expert system. 
One of the factors identified in this framework was the need to segment and group the target points into surfaces for which 
generic camera configurations are known. 
This paper builds on work presented by Mason (1994) and Mason and Kepuska (1991), in particular the investigation of 
whether proximity and uniformity are appropriate criteria for the generalisation of target fields into combinations of 
planes, cylinders, spheres and cones. Several surface features are reviewed as appropriate indicators of uniformity. The 
maximum and minimum curvatures and a function of the surface normal coefficients have been selected as the most 
appropriate uniformity indicators for this evaluation of the uniformity and proximity model. Several different 
computational procedures which employ uniformity measures to group and/or classify points are reviewed. The paper 
details the further development of one of these procedures for the generalisation of target fields using uniformity and 
  
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proximity. 
1. NETWORK DESIGN. 
1.1 Network Design For Complicated Objects. 
The problems to be addressed in the design of 
photogrammetric surveys were identified by Grafarend (1974) 
as being four levels of design. This classification of design 
problems was also adopted by Fraser (1984) and is as follows: 
Zero-Order Design (ZOD) : the datum problem. 
First-Order Design (FOD) : the configuration problem. 
Second-Order Design (SOD) : the weight problem. 
Third-Order Design (TOD) : the densification problem. 
The nature of the object, physical constraints of the workspace 
and the limitations of available equipment are critical to the 
FOD problem and thus the accuracy that can be achieved from 
the network. The research presented in this paper relates to the 
automation of the FOD process. 
The ten constraints and considerations associated with FOD 
were dealt with by Mason (1994). These same constraints and 
considerations were presented by Fraser (1984, 1989), 
however in these earlier articles they were grouped and treated 
differently. The ten constraints all limit the placement of 
sensors (cameras) within the workspace. Several of these 
network design constraints may conflict (Fraser 1992), and the 
best compromise is sought when designing an imaging 
network. 
When designing imaging networks for simple objects a formal 
design process may not be necessary. It is often possible for 
the photogrammetrist to design an ideal imaging network 
simply by viewing the object and its survey site. Design by 
inspection however, requires significant skill and knowledge. 
For complicated objects, the network design by simulation 
process allows for the theoretical precision of object point co- 
ordinates to be quantified prior to the actual measurement 
taking place and is virtually mandatory for complex objects 
(Fraser and Mallison 1992). The simulation process assists the 
designer in dealing with the many interrelated and competing 
design considerations of an imaging network required for the 
survey of a complex object. A limitation of this design by 
simulation process is that expertise is generally needed to 
efficiently handle challenging cases (Mason 1994). The 
requirement for expertise has meant close range analytical 
photogrammetry has rarely been applied other than by 
experienced photogrammetrists (Mason 1994). 
1.2 Expert Systems For Network Design. 
Expert systems are computer systems designed to simulate the 
problem-solving behaviour of a human who is an expert in a 
narrow domain (Denning 1986). The design of strong imaging 
networks (FOD) meets the prerequisites of a task suitable for 
expert system development (Mason 1994). Expert systems 
would play an important role in the development of automated 
network design systems (Mason 1994). The advantage of such 
an automated system would be to reduce the need for expertise 
in close range analytical photogrammetric network design, 
apart from the survey of particularly complicated objects. 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B5. Vienna 1996