Image Processing Reference
In-Depth Information
among n possible decisions d 1 ,d 2 ,...,d n . The main steps we have to achieve in order
to build the fusion process are as follows:
1) modeling: this step includes choosing a formalism and expressions for the ele-
ments of information we wish to fuse within this formalism. This modeling can be
guided by additional information (regarding the information and the context or the
field). Let us assume, to give the reader a better idea, that each source S j provides
an element of information represented by M i regarding the decision d i . The form
of M i depends of course on which formalism was chosen. It can, for example, be a
distribution in a numerical formalism, or a formula in a logical formalism;
2) estimation: most models require an estimation phase (for example, all of the
methods that use distributions). Again, the additional information can come into play;
3) combination: this step involves the choice of an operator, compatible with the
modeling formalism that was chosen, and guided by the additional information;
4) decision: this is the final step of fusion, which allows us to go from information
provided by the sources to the choice of a decision d i .
We will not go into further detail about these steps here because it would require
discussing formalisms and technical aspects. This will be the subject of the following
chapters.
The way these steps are organized defines the fusion system and its architecture.
In the ideal case, the decision is made based on all of the M i , for all of the sources
and all of the decisions. This is referred to as global fusion. In the global model, no
information is overlooked. The complexity of this model and of its implementation
leads to the development of simplified systems, but with more limited performances
[BLO 94].
A second model thus consists of first making local decisions for each source sepa-
rately. In this case, a decision d ( j ) is made based on all of the information originating
from the source S j only. This is known as a decentralized decision. Then, in a second
step, these local decisions are fused into a global decision. This model is the obvi-
ous choice when the sources are not available simultaneously. It provides answers
rapidly because procedures are specific to each source, and can easily be adapted to
the addition of new sources. This type of model benefits from the use of techniques
from adaptive control and often uses distributed architectures. It is also referred to
as decision fusion [DAS 96, THO 90]. Its main drawback comes from the fact that
it poorly describes relations between sensors, as well as the possible correlations or
dependences between sources. Furthermore, this model very easily leads to contra-
dictory local decisions ( d ( j )
= k ) and solving these conflicts implies
arbitration on a higher level, which is difficult to optimize, since the original informa-
tion is no longer available. Models of this type are often implemented for real-time
applications, for example in the military.
= d ( k ) for j
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