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.
Original language | English |
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Pages (from-to) | 99-108 |
Number of pages | 10 |
Journal | Visual Computer |
Volume | 22 |
Issue number | 2 |
DOIs | |
State | Published - Feb 2006 |
Keywords
- Feature extraction
- Mean-shift
- Meshes
- Segmentation