An Improved Skeletion-Based Technique For Three-Dimensional Model Segmentation


Ng, Kok Why (2016) An Improved Skeletion-Based Technique For Three-Dimensional Model Segmentation. PhD thesis, Multimedia University.

Full text not available from this repository.


Three-dimensional (3D) model segmentation has received tremendous attention in recent years, to partition model mesh into meaningful sub-meshes. Hitherto, there is no robust and consistent segmentation method to overcome the problems of under- and over-segmentation for the meaningful components. Many existing methods require either user-input seeding for the number of segments or to apply minima rules to approximate the meaningful components. Some methods excel only in a narrow range of models. Their methods are vague, sensitive to model shape (unstable) and tedious (duplicated processes). Slinky-based segmentation (SBS) with improved skeleton method is proposed in this thesis to automatically and consistently identify meaningful features of a model. The method is robust on any input model shape. The algorithm begins with voxelization and surface-reconstruction on the input model to get rid of the irregular meshes. Laplacian-based Contraction method is adapted to shrink the model into triangular skeleton.

Item Type: Thesis (PhD)
Additional Information: Call No.: TA1638.4 .N45 2016
Uncontrolled Keywords: Image segmentation
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 16 Mar 2018 16:47
Last Modified: 16 Mar 2018 16:47


Downloads per month over past year

View ItemEdit (login required)