Coherent Laplacian 3D 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 also preserved, while the 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 then proposed in which 3D shapes are clustered after embedding, clusters are propagated along time, and merge 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 3D by EM clustering and ISOMAP-based clustering: robustness and effects of topology transitions are discussed.