Coherent Laplacian 3-D protrusion segmentation

In this paper, an analysis of locally linear embedding (LLE) in the context of clustering is developed. As LLE conserves the local affine coordinates of points, shape protrusions as high-curvature regions of the surface are preserved. Also, LLEpsilas covariance constraint acts as a force stretching those protrusions and making them wider separated and lower dimensional. A novel scheme for unsupervised body-part segmentation along time sequences is thus proposed in which 3-D shapes are clustered after embedding. Clusters are propagated in time, and merged or split in an unsupervised fashion to accommodate changes of the body topology. Comparisons on synthetic, and real data with ground truth, are run with direct segmentation in 3-D by EM clustering and ISOMAP-based clustering. Robustness and the effects of topology transitions are discussed.