DocumentCode :
926778
Title :
VC-dimension of exterior visibility
Author :
Isler, Volkan ; Kannan, Sampath ; Daniilidis, Kostas ; Valtr, Pavel
Author_Institution :
Dept. of Comput. & Inf. Sci., Pennsylvania Univ., Philadelphia, PA, USA
Volume :
26
Issue :
5
fYear :
2004
fDate :
5/1/2004 12:00:00 AM
Firstpage :
667
Lastpage :
671
Abstract :
In this paper, we study the Vapnik-Chervonenkis (VC)-dimension of set systems arising in 2D polygonal and 3D polyhedral configurations where a subset consists of all points visible from one camera. In the past, it has been shown that the VC-dimension of planar visibility systems is bounded by 23 if the cameras are allowed to be anywhere inside a polygon without holes. Here, we consider the case of exterior visibility, where the cameras lie on a constrained area outside the polygon and have to observe the entire boundary. We present results for the cases of cameras lying on a circle containing a polygon (VC-dimension=2) or lying outside the convex hull of a polygon (VC-dimension=5). The main result of this paper concerns the 3D case: We prove that the VC-dimension is unbounded if the cameras lie on a sphere containing the polyhedron, hence the term exterior visibility.
Keywords :
cameras; computational geometry; set theory; visibility; 2D polygonal configurations; 3D polyhedral configurations; Vapnik-Chervonenkis dimension; camera; exterior visibility; planar visibility system; subset; Art; Broadband antennas; Broadband communication; Cameras; Inspection; Receiving antennas; Rendering (computer graphics); Robot vision systems; Sampling methods; Surveillance; Algorithms; Artificial Intelligence; Cluster Analysis; Computer Simulation; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2004.1273987
Filename :
1273987
Link To Document :
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