ACM Home Page
Please provide us with feedback. Feedback
Online geometric reconstruction
Full text PdfPdf (248 KB)
Source Annual Symposium on Computational Geometry archive
Proceedings of the twenty-second annual symposium on Computational geometry table of contents
Sedona, Arizona, USA
SESSION: Session 10 (wednesday, june 7th--10:40 am-12:00 pm) table of contents
Pages: 386 - 394  
Year of Publication: 2006
ISBN:1-59593-340-9
Authors
Bernard Chazelle  Princeton University, Princeton, NJ
C. Seshadhri  Princeton University, Princeton, NJ
Sponsors
ACM: Association for Computing Machinery
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 6,   Downloads (12 Months): 21,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1137856.1137912
What is a DOI?

ABSTRACT

We investigate a new class of geometric problems based on the idea of online error correction. Suppose one is given access to a large geometric dataset though a query mechanism; for example, the dataset could be a terrain and a query might ask for the coordinates of a particular vertex or for the edges incident to it. Suppose, in addition, that the dataset satisfies some known structural property P (eg, monotonicity or convexity) but that, because of errors and noise, the queries occasionally provide answers that violate P. Can one design a filter that modifies the query's answers so that (i) the output satisfies P; (ii) the amount of data modification is minimized? We provide upper and lower bounds on the complexity of online reconstruction for convexity in 2D and 3D.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Alon, N., Spencer, J. The probabilistic method, John Wiley, 2nd edition, 2000.
 
2
 
3
 
4
Agarwal, P.K., Har-Peled, S., Mustafa, N., Wang, Y. Near-linear time approximation algorithms for curve simplification, to appear in Algorithmica.
 
5
Agarwal, P.K., Matoušek, J. Dynamic half-space searching and its applications, Algorithmica 14 (1995), 325--345.
 
6
 
7
Ailon, N., Chazelle, B., Comandur, S., Liu, D. Estimating the distance to a monotone function, Proc. RANDOM (2004), 229--236.
 
8
Ailon, N., Chazelle, B., Comandur, S., Liu, D. Property preserving data reconstruction, Proc. ISAAC (2004), 16--27.
 
9
10
 
11
12
 
13
Chvatal, V., Klincsek, G. Finding largest convex subsets, Congressus Numerantium: 29 (1980), 453--460.
 
14
 
15
16
 
17
 
18
19
 
20
 
21
22
 
23
Lipton, R.J., Tarjan, R.E. A separator theorem for planar graphs, SIAM Journal on Applied Mathematics 36 (1979), 177--189.
 
24
Lipton, R.J., Tarjan, R.E. Applications of a planar separator theorem, SIAM J. Comput. 9 (1980), 615--627.
25
 
26
 
27

Collaborative Colleagues:
Bernard Chazelle: colleagues
C. Seshadhri: colleagues