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Heat Kernel Signature (HKS) is a powerful tool for shape analysis for its multi useful properties and has been successfully used in many correspondence tasks. However, the shape's feature detection is an empirical way since HKS depends on the time scale, which is not the intrinsic property of the shape. In order to eliminate the effects of time ambiguity, a novel HKS based feature extraction algorithm...
In shape analysis, scaling factors have a great influence on the results of non-rigid shape retrieval and comparison. In order to eliminate the scale ambiguity in shape acquisition and other cases, a method with scale-invariant property is required for shape analysis. The mapping method previously proposed only preserves geodesic distances between pair wise points. In this paper, a Scale-invariant...
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