  Motion segmentation is a challenging problem that seeks to identify
independent motions in two or several input images. This paper introduces the
first algorithm for motion segmentation that relies on adiabatic quantum
optimization of the objective function. The proposed method achieves on-par
performance with the state of the art on problem instances which can be mapped
to modern quantum annealers.
