Image Processing Reference
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fundamental. It is therefore useful to rely on other forms of representing information in
order to increase the model's reliability by considering information of smaller mean-
ing, or by adding mechanisms for sorting, windowing, etc., to authorize this semantic
information to be taken into account.
A certain number of difficulties in data fusion are caused by generic problems that
are independent of theoretical frameworks.
The first problem is how knowledge, or the lack of knowledge, is modeled (mean-
ing of the information and semantic representation). As we will see, there are several
The second generic problem involves the method of information fusion and the
choice of mechanisms for information management. The problems we are discussing
here involve reliability and/or data association. These are questions related to the con-
cept of uncertainty. The “right” method for combining information necessarily takes
into account the imprecision relative to each source. Let us note that the choice of
mechanisms strongly depends on how knowledge is modeled because either informa-
tion is reliably modeled and the combinations are rather simple, or the model lacks in
reliability and, in that case, additional focus is needed on the mechanisms in order to
take into account the reliability problems during the combination phase.
Finally, a third difficulty lies in the choice of evaluation criteria for the quality of
classifiers. This is because performance in terms of proper classification rates is not, by
itself, a sufficient criterion, hence the necessity of evaluating a classifier's robustness,
in other words how well performances rate when the model strays from reality.
2.2. Objectives of fusion in signal processing
Let us recall Definition 1.1 from the previous chapter: fusion consists of combining
information originating from several sources in order to improve decision making. In
the field of signal processing, the goal of information fusion is to obtain a system to
assist decision making, whose main quality (among others) is to be robust when faced
with various imprecisions, uncertainties and forms of incompleteness regarding the
information sources.
The basic fusion mechanism is described in Chapter 1. It is comprised of four
sequential phases, i.e. a modeling phase, an estimation phase, the actual combination
phase and a decision phase. A fusion system is then comprised of a collection of
different basic mechanisms depending on the problem we are dealing with. We will
now discuss the three major categories of problems that information fusion techniques
attempt to solve in the field of signal processing.
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