No / Both prominent feature points and geodesic distance
are key factors for mesh segmentation. With these two factors,
this paper proposes a graph cut based mesh segmentation
method. The mesh is first preprocessed by Laplacian smoothing.
According to the Gaussian curvature, candidate feature points
are then selected by a predefined threshold. With DBSCAN
(Density-Based Spatial Clustering of Application with Noise), the
selected candidate points are separated into some clusters, and
the points with the maximum curvature in every cluster are
regarded as the final feature points. We label these feature points,
and regard the faces in the mesh as nodes for graph cut. Our
energy function is constructed by utilizing the ratio between the
geodesic distance and the Euclidean distance of vertex pairs of
the mesh. The final segmentation result is obtained by minimizing
the energy function using graph cut. The proposed algorithm is
pose-invariant and can robustly segment the mesh into different
parts in line with the selected feature points.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/8220 |
| Date | January 2015 |
| Creators | Liu, L., Sheng, Y., Zhang, G., Ugail, Hassan |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Conference paper, No full-text available in the repository |
Page generated in 0.0041 seconds