Image Processing Reference
In-Depth Information
Fuzzy fusion operators fall into three categories. T-norms, which generalize set
intersection to fuzzy sets, are conjunctive CICB operators, since for any t-norm
t
,we
have:
[0
,
1]
2
,t
(
x, y
)
∀
(
x, y
)
∈
≤
min(
x, y
)
.
On the other hand, t-conorms which generalize union are disjunctive CICB opera-
tors, since for any t-conorms
T
,wehave:
[0
,
1]
2
,T
(
x, y
)
∀
(
x, y
)
∈
≥
max(
x, y
)
.
Mean operators are also CICBs and have a compromise behavior, since they verify:
[0
,
1]
2
,
min(
x, y
)
∀
(
x, y
)
∈
≤
m
(
x, y
)
≤
max(
x, y
)
.
Let us note that Bayesian fusion, in which the operator involved is a product, and
fusion of belief functions using Dempster's orthogonal sum are also conjunctive.
In the CIVB operator class we have, for example, certain symmetric sums. Gener-
ally speaking, any associative symmetric sum
σ
(except for medians) has the following
behavior [DUB 88]:
- conjunctive if max(
x, y
)
<
1
/
2:
σ
(
x, y
)
≤
min(
x, y
);
- disjunctive if min(
x, y
)
>
1
/
2:
σ
(
x, y
)
≥
max(
x, y
);
- compromise if
x
≤
1
/
2
≤
y
:
x
≤
σ
(
x, y
)
≤
y
(and the opposite inequality if
y
≤
1
/
2
≤
x
).
Non-associative symmetric sums also have a variable behavior, but according to
less simple rules [BLO 96b].
In the CIVB operator class, we also have the operators suggested in the MYCIN
system for combining certainty factors [SHO 75].
Examples of CD operators are found in possibility theory. Earlier, we presented
operators that depended on an overall measure of the conflict between two sources of
information [DUB 92a], which are applicable to cases where one of the two elements
of information is reliable, but where we do not know which one, so that:
- they are conjunctive if the sources are consonant (low conflict): in this case, the
two sources are necessarily reliable and therefore the operator can be strict;
- they are disjunctive if the sources are dissonant (high conflict): a disjunction then
favors all of the possibilities provided by both sources;
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