- Bayesian framework, Markov Random Fields
- Random variables: pixels
- Local interaction

- Correlated noise ?
- No geometric constraints

*Image space:**T**Object space:**U*(object parameters)*Objet support:**R*(*u*)*Configuration:*

*Configurations space:*

is a measurable space, with measure ,
corresponding
to a **uniform Poisson process**.

**Poisson process :**

**Markov Object Process : **

Simulation using a

**Description:**

**general**scheme**every transition**can be defined: at each step, a transition from the current state*x*to a new state*y*is proposed- the transition is accepted with a probability
depending on an
**acceptance ratio**which depends on the law*f* - Challenge: define ``good'' transitions

At step *t*, *X*_{t}=*x* :

- 1.
- simulate
*y*with density*q*(.,.) - 2.
- compute:

- 3.
- with probability
,
set
*X*_{t+1}=*y*, otherwise*X*_{t+1}=*x*