Building and training radiographic models for flexible object identification from incomplete data

The authors address the problem of identifying the projection of an object from incomplete data extracted from its radiographic image. They assume that the unknown object is a particular sample of a flexible object. Their approach consists first in designing a deformation model able to represent and to simulate a great variety of samples of the flexible object radiographic projection. This modellisation is achieved using a training set of complete data. Then, given the incomplete data, the identification task consists in estimating the observed object using the deformation model. The proposed modelling extracts from the training set, not only the deformation modes, but also other relevant information (such as probability distributions on the deformations, relations between deformations) to use it to regularise the identification step