Partition, level set approach
The image is considered as a function
(where
is an open subset of
)
We denote
.
The collection of open sets
forms a partition of
if and only if
In order to get a functional formulation rather than a set formulation, we suppose that for each
there exists a lipschitz function
such that:
Regularization
In our equations, there will appear some Dirac and Heaviside distributions and
.
In order that all the expressions we write have a mathematical meaning, we use the classical regular approximations of these distributions (see figure 2):
Figure 3 shows how the regions are defined by these distributions and the level sets.
Functional
Our functional will have three terms:
1) A partition term:
![]() |
(2.1) |
2) A regularization term:
![]() |
(2.2) |
![]() |
(2.3) |
3) A data term:
![]() |
(2.4) |
The functional we want to minimize is the sum of the three previous terms:
![]() |
(2.5) |