Name
Abstract | ||
|---|---|---|
In this paper, we propose using curvatures in digital space for 3D object
analysis and recognition. Since direct adjacency has only six types of digital
surface points in local configurations, it is easy to determine and classify
the discrete curvatures for every point on the boundary of a 3D object. Unlike
the boundary simplicial decomposition (triangulation), the curvature can take
any real value. It sometimes makes difficulties to find a right value for
threshold. This paper focuses on the global properties of categorizing
curvatures for small regions. We use both digital Gaussian curvatures and
digital mean curvatures to 3D shapes. This paper proposes a multi-scale method
for 3D object analysis and a vector method for 3D similarity classification. We
use these methods for face recognition and shape classification. We have found
that the Gaussian curvatures mainly describe the global features and average
characteristics such as the five regions of a human face. However, mean
curvatures can be used to find local features and extreme points such as nose
in 3D facial data. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34732-0_4 | international workshop on combinatorial image analysis |
Keywords | DocType | Volume |
face recognition,computational geometry,mean curvature,gaussian curvature,extreme point,discrete mathematics | Conference | abs/0910.4 |
Citations | PageRank | References |
1 | 0.40 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Chen | 1 | 77 | 8.25 |
| Soma Biswas | 2 | 409 | 28.08 |