DocumentCode :
3672185
Title :
Continuous Visibility Feature
Author :
Guilin Liu;Yotam Gingold;Jyh-Ming Lien
Author_Institution :
Department of Computer Science, George Mason University, Fairfax, VA, USA, 22030
fYear :
2015
fDate :
6/1/2015 12:00:00 AM
Firstpage :
1182
Lastpage :
1190
Abstract :
In this work, we propose a new type of visibility measurement named Continuous Visibility Feature (CVF). We say that a point q on the mesh is continuously visible from another point p if there exists a geodesic path connecting p and q that is entirely visible by p. In order to efficiently estimate the continuous visibility for all the vertices in a model, we propose two approaches that use specific CVF properties to avoid exhaustive visibility tests. CVF is then measured as the area of the continuously visible region. With this stronger visibility measure, we show that CVF better encodes the surface and part information of mesh than the tradition line-of-sight based visibility. For example, we show that existing segmentation algorithms can generate better segmentation results using CVF and its variants than using other visibility-based shape descriptors, such as shape diameter function. Similar to visibility and other mesh surface features, continuous visibility would have many applications.
Keywords :
"Shape","Benchmark testing","Joining processes","TV","Feature extraction","Semantics","Level measurement"
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2015.7298722
Filename :
7298722
Link To Document :
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