Name
Abstract | ||
|---|---|---|
It is important to determine 3D shape properties of a population of 3D mesh models in biomedical imaging issues. In contrast to conventional 3D shape analysis techniques focusing on applications like shape matching and shape retrieval, we propose in this paper a strategy capable to collect statistical information of multiple triangular mesh models. Our method operates in a coarse-to-fine fashion based on wavelet synthesis. Hence, its analysis result can be invariant against the triangular tiling of the input mesh model. This characteristic enables us to make a comparison among multiple mesh models simultaneously. The experiment results show that our method can extract 3D shape components of each observation scale and is efficient in estimating an average shape and visualizing 3D shape variability of multiple triangular mesh models. |
Year | Venue | Keywords |
|---|---|---|
2017 | IEEE Global Conference on Signal and Information Processing | wavelet transform,triangular mesh model,3D shape analysis,multiscale representation |
Field | DocType | ISSN |
Population,Polygon mesh,Computer science,Mathematical analysis,Algorithm,Invariant (mathematics),Wavelet packet decomposition,Triangle mesh,Wavelet,Wavelet transform,Shape analysis (digital geometry) | Conference | 2376-4066 |
Citations | PageRank | References |
1 | 0.37 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao-Chiang Shao | 1 | 9 | 4.97 |
| Wen-Liang Hwang | 2 | 429 | 58.03 |