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dc.contributor.authorLiu, L.
dc.contributor.authorSheng, Y.
dc.contributor.authorZhang, G.
dc.contributor.authorUgail, Hassan
dc.date.accessioned2016-04-28T08:47:20Z
dc.date.available2016-04-28T08:47:20Z
dc.date.issued2015
dc.identifier.citationLiu L, Sheng Y, Zhang G and Ugail H (2015) Graph Cut Based Mesh Segmentation Using Feature Points and Geodesic Distance. In: Proceedings of the 2015 International Conference on Cyberworlds. Gotland, Sweden.115-120.en_US
dc.identifier.urihttp://hdl.handle.net/10454/8220
dc.descriptionNoen_US
dc.description.abstractBoth 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.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttp://dx.doi.org/10.1109/CW.2015.31en_US
dc.subjectGaussian processes; Feature selection; Graph theory; Image segmentation; Pattern clustering; Smoothing methods; Solid modelling; Arrays; Feature extraction; Laplace equations; Three-dimentional displays; Yttriumen_US
dc.titleGraph Cut Based Mesh Segmentation Using Feature Points and Geodesic Distanceen_US
dc.status.refereedYesen_US
dc.typeConference paperen_US
dc.type.versionNo full-text available in the repositoryen_US


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