Model Parameter Estimation Using Simulated Annealing

In image analysis and image processing most methods require the selection of model parameters such as thresholds. As this task can be rather complex, it is often conducted by a human operator. Here a generally applicable automatic estimation scheme for model parameters is proposed. The parameter space is explored to find a global optimum by simulated annealing controlled by a divergence measure resulting from a comparison of processing results and a training data set, i.e. "ground truth". In comparison with an interactive selection of parameter values the automatic estimation has advantages when parameters are to be determined from many data sets, or in a parameter space which is not intuitively accessible. Furthermore, interdependencies between parameters can be investigated, e.g. to avoid overparameterization. The parameter estimation method can be interpreted as a means of learning in order to adapt the analysis method to different data sets. The estimation method is applied to two ..