An application of support vector machines to an image interpretation problem

We describe an approach to an image interpretation problem which is based on a combination of modelling and machine learning. It involves inferring properties of the image formation process from the structures present in the image. Due to the possible complexity of the structures and the ill-posed nature of the problem, modelling does not always adequately explain the data. We use support vector machines to supervise the model fitting process by training them to identify those situations where modelling will fail