SCALE-SPACE FRAMEWORK FOR EFFICIENT SEGMENTATION OF POINT-SAMPLED GEOMETRY
|
Accession number;05A0212990
|
| Title;SCALE-SPACE FRAMEWORK FOR EFFICIENT SEGMENTATION OF POINT-SAMPLED GEOMETRY |
| Author;
LAGA H
(Tokyo Inst. Technol., Tokyo, Jpn)
TAKAHASHI H
(Tokyo Inst. Technol., Tokyo, Jpn)
NAKAJIMA M
(Tokyo Inst. Technol., Tokyo, Jpn)
|
Journal Title;IEIC Technical Report (Institute of Electronics, Information and Communication Engineers)
|
Journal Code:S0532B
|
ISSN:0913-5685
|
|
VOL.104;NO.545(IE2004 145-173);PAGE.109-114(2005)
|
| Figure&Table&Reference;FIG.4, TBL.1, REF.23 |
| Pub. Country;Japan |
| Language;English |
| Abstract;In this paper, we introduce a novel framework for analyzing and segmenting point-sampled 3D objects. Our algorithm computes a decomposition of a given point set surface into meaningful components, which are delimited by line features and deep concavities (points of high negative curvature). Central to our method is the combination of the scale-space theory, line feature extraction and convexity measure to compute a new surface classifier which is then used in an anisotropic diffusion process via partial differential equations (PDEs). The algorithm avoids the misclassifications due to fuzzy and soft line features. It operates directly on points requiring no vertex connectivity information. We demonstrate and discuss its performance on a collection of point sampled 3D objects including CAD and natural models. (author abst.) |
|
|
|
Related Articles;
|
|
