Title
Accurate Principal Directions Estimation in Discrete Surfaces
Abstract
Accurate local surface geometry estimation in discrete surfaces is an important problem with numerous applications. Principal curvatures and principal directions can be used in applications such as shape analysis and recognition, object segmentation, adaptive smoothing, anisotropic fairing of irregular meshes, and anisotropic texture mapping. In this paper, a novel approach for accurate principal direction estimation in discrete surfaces is described. The proposed approach is based on local directional curve sampling of the surface where the sampling frequency can be controlled. This local model has a large number of degrees of freedoms compared with known techniques and so can better represent the local geometry. The proposed approach is quantitatively evaluated and compared with known techniques for principal direction estimation. In order to perform an unbiased evaluation in which smoothing effects are factored out, we use a set of randomly generated Bezier surface patches for which the principal directions can be computed analytically.
Year
DOI
Venue
2005
10.1109/3DIM.2005.14
3DIM
Keywords
Field
DocType
known technique,accurate principal,local geometry,principal curvature,principal direction,local directional curve,principal direction estimation,accurate principal direction estimation,discrete surfaces,discrete surface,accurate local surface geometry,sampling frequency,shape,image recognition,computational geometry,anisotropic magnetoresistance,texture mapping,solid modeling,geometry,image segmentation,degree of freedom,frequency,estimation theory,shape analysis,sampling methods,principal curvatures
Texture mapping,Computer vision,Computer science,Computational geometry,Bézier surface,Principal curvature,Image segmentation,Smoothing,Artificial intelligence,Estimation theory,Shape analysis (digital geometry)
Conference
ISBN
Citations 
PageRank 
0-7695-2327-7
1
0.35
References 
Authors
18
2
Name
Order
Citations
PageRank
Gady Agam139143.99
Xiaojing Tang2381.75