Image Processing Reference

In-Depth Information

conditional probabilities P(w/Ci)

P(w/Ci)

P(Ci)

P(w/river)

P(w/city) P(city)

P(w/city)

P(w/river)

P(river)

gray level space

gray level space

Figure 6.2.
Left: conditional probabilities of the cities and rivers in the texture image on XS2.

Right: probabilities conditional to the two classes multiplied by their a priori probability

P
(
city
) = 11%

and

P
(
river
)=2%

Figure 6.3 shows the fusion result, for an
a priori
maximum criterion. The river is

superimposed in white over the original image, as well as the contours of the urban

areas. The rest corresponds to the class
C
3
.

6.9. Probabilistic fusion methods applied to target motion analysis

After seeing an example of Bayesian fusion in image processing, we are now going

to see other probabilistic methods based on different concepts applied to a traditional

problem in signal processing, i.e. that of target motion analysis. After a general pre-

sentation of target motion analysis, we will examine in detail some aspects that are

more relevant to data fusion. In particular, we will focus on showing what the basic

techniques of target motion analysis can contribute to a detection system.

6.9.1.
General presentation

First of all, let us point out the goal of target motion analysis: determining, based

on measurements (observations), the trajectory of a moving object. This essentially

involves a framework in which a dynamic system is partially observed. A traditional

and historical example is the estimation of a planet's trajectory, based on optical obser-

vations (angle measurements). Below are the most widespread types of measurements

in target motion analysis:

- angles (sonar, ESM, infrared, etc.);

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