  We present a new approach to rigid-body motion segmentation from two views.
We use a previously developed nonlinear embedding of two-view point
correspondences into a 9-dimensional space and identify the different motions
by segmenting lower-dimensional subspaces. In order to overcome nonuniform
distributions along the subspaces, whose dimensions are unknown, we suggest the
novel concept of global dimension and its minimization for clustering subspaces
with some theoretical motivation. We propose a fast projected gradient
algorithm for minimizing global dimension and thus segmenting motions from
2-views. We develop an outlier detection framework around the proposed method,
and we present state-of-the-art results on outlier-free and outlier-corrupted
two-view data for segmenting motion.
