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Parallel Chains, Delayed Rejection and Reversible Jump MCMC for Object Recognition

We tackle the problem of object recognition using a Bayesian approach. A marked point process [1] is used as a prior model for the (unknown number of) objects. A sample is generated via Markov chain Monte Carlo (MCMC) techniques using a novel combination of Metropolis-coupled MCMC (MCMCMC) [2] and the Delayed Rejection Algorithm (DRA) [4]. The method is illustrated on some synthetic data containing simple geometrical objects.