Image Processing Reference
In-Depth Information
unknown because of several factors, such as random false alarms in detections, clut-
ter, interfering targets, traps and other countermeasures. The main models used in
this field are either deterministic (based on classic hypothesis tests), or probabilistic
models (essential Bayesian) [BAR 88, LEU 96, ROM 96]. The most common method
[BAR 88] relies on the Kalman filter with a Gaussian hypothesis. More recently, other
estimation methods have been suggested, such as the Interactive Multiple Model esti-
mator (IMM), which can adapt to different types of motion and reduce noise, while
preserving a good accuracy in estimating states [YED 97]. This shows how the prob-
lems we come across can be quite different from those covered by Definition 1.1.
Fusion and data mining. Data mining consists of extracting relevant parts of infor-
mation and data, which can be, for example, special data (in the sense that it has spe-
cific properties), or rare data. It can be distinguished from fusion that tries to explain
where the objective is to find general trends, or from fusion that tries to generalize
and lead to more generic knowledge based on data. We will not be considering data
mining as a fusion problem.
1.3. General characteristics of the data
In this section, we will briefly describe the general characteristics of the informa-
tion we wish to fuse, characteristics that have to be taken into account in a fusion
process. More detailed and specific examples will be given for each field in the fol-
lowing chapters.
A first characteristic involves the type of information we wish to fuse. It can con-
sist of direct observations, results obtained after processing these observations, more
generic knowledge, expressed in the form of rules for example, or opinions of experts.
This information can be expressed either in numerical or symbolic form (see section
1.4). Particular attention is needed in choosing the scale used for representing the
information. This scale should not necessarily have any absolute significance, but it at
least has to be possible to compare information using the scale. In other words, scales
induce an order within populations. This leads to properties of commensurability, or
even of normalization.
The different levels of the elements of information we wish to fuse are also a
very important aspect. Usually, the lower level (typically the original measurements)
is distinguished from a higher level requiring preliminary steps, such as processing,
extracting primitives or structuring the information. Depending on the level, the con-
straints can vary, as well as the difficulties. This will be illustrated, for example, in the
case of image fusion in Chapter 3.
Other distinctions in the types of data should also be underlined, because they give
rise to different models and types of processing. The distinction between common and
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