Mesh analysis using geodesic mean-shift |
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Authors: | Ariel Shamir Lior Shapira Daniel Cohen-Or |
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Affiliation: | (1) The interdisciplinary center, Efi Arazi School of Computer Science, Israel;(2) School of Computer Science, Tel-Aviv University, Israel |
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Abstract: | In this paper, we introduce a versatile and robust method for analyzing the feature space associated with a given mesh surface.
The method is based on the mean-shift operator, which was shown to be successful in image and video processing. Its strength
lies in the fact that it works in a single joint space of geometry and attributes called the feature-space. The mean-shift procedure works as a gradient ascend finding maxima of an estimated probability density function in feature-space.
Our method for using the mean-shift technique on surfaces solves several difficulties. First, meshes as opposed to images
do not present a regular and uniform sampling of domain. Second, on surface meshes the shifting procedure must be constrained
to stay on the surface and preserve geodesic distances. We define a special local geodesic parameterization scheme, and use
it to generalize the mean-shift procedure to unstructured surface meshes. Our method can support piecewise linear attribute
definitions as well as piecewise constant attributes. |
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Keywords: | Mean-shift Meshes Segmentation Feature extraction |
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