Name
Abstract | ||
|---|---|---|
Similarity measuring is a key problem for 3D model retrieval. We propose a novel shape descriptor "thickness histogram" (TH) by uniformly estimating thickness of a model using statistical methods. It is translation and rotation-invariant, discriminative to different shapes, and very efficient to compute with the shape distribution (SD) proposed by Osada etc. For high performance of the retrieval, we propose a robust method for translating the directional form of the statistical distribution to the harmonic representation. By summing up energies at different frequencies, a matrix shape signature is formed to provide an exhaustive characterization of 3D geometry. Experiments show that the performance of the statistical harmonic representation is among the top ones of existing shape descriptors. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/TDPVT.2004.1335410 | 3DPVT |
Keywords | Field | DocType |
harmonic representation,high performance,statistical distributions,different frequency,matrix shape signature,statistical harmonic representation,similarity measuring,shape descriptors,statistical distribution,model retrieval,visual databases,different shape,fractals,3d model retrieval,computational geometry,thickness histogram,statistical distribution method,feature extraction,3d geometry,solid modelling,shape distribution,statistical method,content-based retrieval,novel shape descriptor | Active shape model,Histogram,Pattern recognition,Matrix (mathematics),Computational geometry,Harmonic,Feature extraction,Probability distribution,Artificial intelligence,Discriminative model,Mathematics | Conference |
ISBN | Citations | PageRank |
0-7695-2223-8 | 8 | 0.87 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Liu | 1 | 116 | 22.97 |
| Jiantao Pu | 2 | 277 | 23.12 |
| Hongbin Zha | 3 | 2206 | 183.36 |
| Weibin Liu | 4 | 71 | 19.97 |
| Yusuke Uehara | 5 | 62 | 8.15 |