![](bg6.png)
5 EVALUATING THE RESULTS OF
THE LEARNING PROCESS
By updating the restrictions of the refinement
process the MEN can be applied within the model
structure generation in order to employ the necessary
feedback for the evaluation. In case of missing or
unreliable feedback caused by input data noise or
contradictions in the knowledge base a new analysis
by synthesis algorithm derived from the global
model optimisation process has been implemented to
overcome the assignment and correlation problem.
Based on the actual state of the hypothesis the
synthesis algorithm analyses the domain and scene
specific knowledge step by step considering every
update of incoming information until a satisfying
level is reached for every corresponding evaluation.
Because of the complex and dynamic behaviour of
the underlying restrictions (Hepner 1994) the
analysis by synthesis concept cannot directly be
derived from the MEN – algorithm structure
independent from the quality of the input
information. For the modelling and construction of
man made rigid objects considering data driven as
well as knowledge based methods the initial
estimation process could be verified in [Buescher
2000] applying several relaxation algorithms. Its
kernel concept however is derived from a static
dedicated evaluation process based on data driven
optimisation methods and therefor hard to
implement within the discussed modelling or
construction tasks
. The goal of this work is the
introduction and implementation of efficient
modelling concepts derived from the MEN method,
which can be controlled dynamically. Starting from
an initial state, in which every element of the
knowledge base will be considered with the same
reliability, the main modelling process, in which the
new relaxation and regression methods will be
applied, analyse and evaluate every subsequent state
of the model and knowledge base with the goal of
eliminating every contradiction between the
different rules of the knowledge system and
satisfying all given criterions as best as possible.
Applied to the input image in Fig. 1 the result of this
process is displayed in Fig. 3.
6 CONCLUSION
In this paper we described a knowledge-based
solution approach for controlling 3D - Modelling
and reconstruction for multiple different issues in the
presence of several restrictions and requirements.
The problem is of interest both to the academic
community (it generalises the problems studied
earlier) and to modelling experts. It captures
additional restrictions and knowledge that exist in
controlling systems. That means, it is not
inconsistent and will become unstable by what has
been modified during the learning process or due to
the extension of the knowledge process. Selflearning
and –organising methods are the benefit of the
whole system. This capability enables the kernel to
overcome the problems which arise from
contradictions and to reach the goal of a high quality
level. The concretising process and instantiation in
the main system ensure, that the knowledge
algorithms have to share their specific results for an
optimal learning effort.
To the best of our knowledge, this is the first
attempt to solve this general problem. Our solution
approach is to divide the knowledge base into
different levels, linking multiple solution strategies
with each other, building up rules and assigning
them to the functional behaviour. We have
implemented the controlling processes as part of a
knowledge based decision system for controlling 3D
modelling and reconstruction. The knowledge based
system is received in the specific marketplace and
currently being used on several CAD stations and
simulation systems in education and industry.
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