DocumentCode
2494947
Title
Multi-scale feature extraction for 3d surface registration using local shape variation
Author
Ho, Huy Tho ; Gibbins, Danny
Author_Institution
Sensor Signal Process. Group, Univ. of Adelaide, Adelaide, SA
fYear
2008
fDate
26-28 Nov. 2008
Firstpage
1
Lastpage
6
Abstract
This paper describes a method for extracting salient local features from 3D models using shape variation which has application to 3D surface registration. In the proposed technique, the surface shape at a point is specified by a quantitative measure known as the shape index. It is invariant to rigid transformations such as translation and rotation. The shape index at a point is calculated at multiple scales by fitting a surface to the local neighbourhoods of different sizes. The local surface variation is then measured by calculating the variation of the shape index of every point in the neighbourhood. Points corresponding to local maxima of surface variation are selected as suitable features. Experimental results of applying the proposed feature extraction method on a variety of 3D models are shown to evaluate the effectiveness and robustness of our approach.
Keywords
feature extraction; image registration; image representation; solid modelling; surface fitting; 3D model; 3D surface registration; local shape variation; multiscale feature extraction; salient local feature extraction; scale-space surface representation; shape index; surface curvature estimation; Application software; Australia; Computer vision; Feature extraction; Robustness; Sensor phenomena and characterization; Shape measurement; Signal processing; Surface fitting; Surface reconstruction; Feature extraction; local curvature; shape index; shape variation; surface registration;
fLanguage
English
Publisher
ieee
Conference_Titel
Image and Vision Computing New Zealand, 2008. IVCNZ 2008. 23rd International Conference
Conference_Location
Christchurch
Print_ISBN
978-1-4244-3780-1
Electronic_ISBN
978-1-4244-2583-9
Type
conf
DOI
10.1109/IVCNZ.2008.4762120
Filename
4762120
Link To Document