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This work introduces a method to hierarchically segment articulated shapes into meaningful parts and to register these parts across populations of nearisometric shapes (e.g. head, arms, legs and fingers of humans in different body postures). The method exploits the isometry invariance of eigenfunctions of the Laplace-Beltrami operator and uses topological features (level sets at important saddles) for the segmentation. Concepts from persistent homology are employed for a hierarchicaldoi:10.1007/s11263-009-0278-1 fatcat:bpura5zsojhermyh2jgisd2r6y