Motion segmentation is aimed at segmenting the feature point trajectories belonging to independently moving objects. Using the affine camera model, the motion segmentation problem can be viewed as a subspace clustering problem—clustering the data points drawn from a union of low-dimensional subspaces.

In this paper model-based segmentation is defined as the assignment of labels to pixels or voxels by matching the a priori known object model to the image data. Labels may have probabilities expressing their uncertainty. Particularly we compare optimization methods with the knowledge-based system approach.