Here we present a new approach to automatically detect and count breeding Greater Flamingos (Phoenicopterus Roseus) on aerial photographs of their colonies. We consider a stochastic approach based on object processes also called marked point processes. The objects represent flamingos which are defined as ellipses. We formulate a Gibbs density, associated with the marked point process of ellipses, which is defined w.r.t a Poisson measure. Thus, the issue is reduced to an energy minimization,

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... e the energy is composed of a regularization term (prior density), which introduces some constraints on the objects and their interactions, and a data term, which links the objects to the features to be extracted in the image. Then, we sample the process to extract the configuration of objects minimizing the energy by a new and fast birth-and-death dynamics, leading to the total number of birds. This approach gives counts with good precision compared to manual counts. Additionally, this approach does not need image pre-processing or supervision of the extraction by an operator thus considerably reducing the overall processing time required to get the estimate.