Name
Abstract | ||
|---|---|---|
When large-scale and complex 3D objects are obtained by range finders, it is often necessary to represent them by algebraic surfaces for such purposes as data compression, multi-resolution, noise elimination, and 3D recognition. Representing the 3D data with algebraic surfaces of an implicit polynomial (IP) has proved to offer the advantages that IP representation is capable of encoding geometric properties easily with desired smoothness, few parameters, algebraic/geometric invariants, and robustness to noise and missing data. Unfortunately, generating a high-degree IP surface for a whole complex 3D shape is impossible because of high computational cost and numerical instability. In this paper we propose a 3D segmentation method based on a cut-and-merge approach. Two cutting procedures adopt low-degree IPs to divide and fit the surface segments simultaneously, while avoiding generating high-curved segments. A merging procedure merges the similar adjacent segments to avoid over-segmentation. To prove the effectiveness of this segmentation method, we open up some new vistas for 3D applications such as 3D matching, recognition, and registration. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1093/ietisy/e91-d.4.1149 | IEICE Transactions |
Keywords | Field | DocType |
segmentation method,implicit polynomials,geometric invariants,implicit polynomial ip,algebraic surface,high-degree ip surface,geometric property,model segmentation,data compression,3d segmentation,noise elimination,surface segment,ip representation,missing data,3d rep- resentation.,algebraic surfaces | Computer vision,Algebraic number,Pattern recognition,Polynomial,Segmentation,Computer science,Algebraic surface,Robustness (computer science),Artificial intelligence,Invariant (mathematics),3-dimensional matching,Data compression | Journal |
Volume | Issue | ISSN |
E91-D | 4 | 1745-1361 |
Citations | PageRank | References |
5 | 0.44 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Zheng | 1 | 159 | 13.62 |
| Jun Takamatsu | 2 | 280 | 51.47 |
| Katsushi Ikeuchi | 3 | 4651 | 881.49 |