Results 1  10
of
39
Spanning Trees and Spanners
, 1996
"... We survey results in geometric network design theory, including algorithms for constructing minimum spanning trees and lowdilation graphs. 1 Introduction This survey covers topics in geometric network design theory. The problem is easy to state: connect a collection of sites by a "good" ..."
Abstract

Cited by 143 (2 self)
 Add to MetaCart
We survey results in geometric network design theory, including algorithms for constructing minimum spanning trees and lowdilation graphs. 1 Introduction This survey covers topics in geometric network design theory. The problem is easy to state: connect a collection of sites by a "good" network. For instance, one may wish to connect components of a VLSI circuit by networks of wires, in a way that uses little surface area on the chip, draws little power, and propagates signals quickly. Similar problems come up in other applications such as telecommunications, road network design, and medical imaging [1]. One network design problem, the Traveling Salesman problem, is sufficiently important to have whole books devoted to it [79]. Problems involving some form of geometric minimum or maximum spanning tree also arise in the solution of other geometric problems such as clustering [12], mesh generation [56], and robot motion planning [93]. One can vary the network design problem in many w...
Improved Incremental Randomized Delaunay Triangulation
, 1997
"... We propose a new data structure to compute the Delaunay triangulation of a set of points in the plane. It combines good worst case complexity, fast behavior on real data, and small memory occupation. The location ..."
Abstract

Cited by 44 (9 self)
 Add to MetaCart
We propose a new data structure to compute the Delaunay triangulation of a set of points in the plane. It combines good worst case complexity, fast behavior on real data, and small memory occupation. The location
The Delaunay hierarchy
 Internat. J. Found. Comput. Sci
"... We propose a new data structure to compute the Delaunay triangulation of a set of points in the plane. It combines good worst case complexity, fast behavior on real data, small memory occupation and the possibility of fully dynamic insertions and deletions. The location structure is organized into s ..."
Abstract

Cited by 35 (5 self)
 Add to MetaCart
We propose a new data structure to compute the Delaunay triangulation of a set of points in the plane. It combines good worst case complexity, fast behavior on real data, small memory occupation and the possibility of fully dynamic insertions and deletions. The location structure is organized into several levels. The lowest level just consists of the triangulation, then each level contains the triangulation of a small sample of the level below. Point location is done by walking in a triangulation to determine the nearest neighbor of the query at that level, then the walk restarts from that neighbor at the level below. Using a small subset (3%) to sample a level allows a small memory occupation; the walk and the use of the nearest neighbor to change levels quickly locate the query.
A Note on Point Location in Delaunay Triangulations of Random Points
, 1998
"... This short note considers the problem of point location in a Delaunay triangulation of n random points, using no additional preprocessing or storage other than a standard data structure representing the triangulation. A simple and easytoimplement (but, of course, worstcase suboptimal) heuristic i ..."
Abstract

Cited by 28 (5 self)
 Add to MetaCart
This short note considers the problem of point location in a Delaunay triangulation of n random points, using no additional preprocessing or storage other than a standard data structure representing the triangulation. A simple and easytoimplement (but, of course, worstcase suboptimal) heuristic is shown to take expected time O(n ).
A compact piecewiselinear Voronoi diagram for convex sites in the plane
 Discrete Comput. Geom
, 1996
"... In the plane, the postofice problem, which asks for the closest site to a query site, and retraction motion planning, which asks for a onedimensional retract of the free space of a robot, are both classtcally solved by computing a Voronoi diagram. When the sites are k disjoint convex sets, we giv ..."
Abstract

Cited by 26 (4 self)
 Add to MetaCart
In the plane, the postofice problem, which asks for the closest site to a query site, and retraction motion planning, which asks for a onedimensional retract of the free space of a robot, are both classtcally solved by computing a Voronoi diagram. When the sites are k disjoint convex sets, we give a compact representation of the Voronoi diagram, using O(k) line segments, that is suficient for logarithmic time postofice location queries and motion planning. If these sets are polygons with n total vertices, we compute this diagram optimally in O ( k log n) deterministic time for the Euclidean metric and in O(k logn logm) deterministic time for the convex distance function defined by a convex mgon. 1
A progressive scheme for stereo matching
 LNCS 2018: 3D Structure from Images  SMILE 2000
, 2001
"... Bruteforce dense matching is usually not satisfactory because the same search range is used for the entire image, yielding potentially many false matches. In this paper, we propose a progressive scheme for stereo matching which uses two fundamental concepts: the disparity gradient limit principle a ..."
Abstract

Cited by 25 (1 self)
 Add to MetaCart
Bruteforce dense matching is usually not satisfactory because the same search range is used for the entire image, yielding potentially many false matches. In this paper, we propose a progressive scheme for stereo matching which uses two fundamental concepts: the disparity gradient limit principle and the least commitment strategy. The first states that the disparity should vary smoothly almost everywhere, and the disparity gradient should not exceed a certain limit. The second states that we should first select only the most reliable matches and therefore postpone unreliable decisions until enough confidence is accumulated. Our technique starts with a few reliable point matches obtained automatically via feature correspondence or through user input. New matches are progressively added during an iterative matching process. At each stage, the current reliable matches constrain the search range for their neighbors according to the disparity gradient limit, thereby reducing potential matching ambiguities of those neighbors. Only unambiguous matches are selected and added to the set of reliable matches in accordance with the least commitment strategy. In addition, a correlation match measure that allows rotation of the match template is used to provide a more robust estimate. The entire process is cast within a Bayesian inference framework. Experimental results illustrate the robustness of our proposed dense stereo matching approach.
A dynamic data structure for 3d convex hull and 2d nearest neighbor queries
 In: Proceedings of the seventeenth ACMSIAM symposium on Discrete algorithm
, 2006
"... We present a fully dynamic randomized data structure that can answer queries about the convex hull of a set of n points in three dimensions, where insertions take O(log 3 n) expected amortized time, deletions take O(log 6 n) expected amortized time, and extremepoint queries take O(log 2 n) worstca ..."
Abstract

Cited by 23 (5 self)
 Add to MetaCart
We present a fully dynamic randomized data structure that can answer queries about the convex hull of a set of n points in three dimensions, where insertions take O(log 3 n) expected amortized time, deletions take O(log 6 n) expected amortized time, and extremepoint queries take O(log 2 n) worstcase time. This is the first method that guarantees polylogarithmic update and query cost for arbitrary sequences of insertions and deletions, and improves the previous O(n ε)time method by Agarwal and Matouˇsek a decade ago. As a consequence, we obtain similar results for nearest neighbor queries in two dimensions and improved results for numerous fundamental geometric problems (such as levels in three dimensions and dynamic Euclidean minimum spanning trees in the plane). 1
On The Randomized Construction Of The Delaunay Tree
, 1991
"... The Delaunay Tree is a hierarchical data structure that was introduced in [BT86]. It is defined from the Delaunay triangulation and, roughly speaking, represents a triangulation as a hierarchy of balls. It allows a semidynamic construction of the Delaunay triangulation of a finite set of n points i ..."
Abstract

Cited by 21 (3 self)
 Add to MetaCart
The Delaunay Tree is a hierarchical data structure that was introduced in [BT86]. It is defined from the Delaunay triangulation and, roughly speaking, represents a triangulation as a hierarchy of balls. It allows a semidynamic construction of the Delaunay triangulation of a finite set of n points in any dimension. In this paper, we prove that a randomized construction of the Delaunay Tree (and thus, of the Delaunay triangulation) can be done in O(n log n) expected time in the plane and in O i n d d 2 e j expected time in ddimensional space. These results are optimal for fixed d. The algorithm is extremely simple and experimental results are given.
Redundancy and coverage detection in sensor networks
 ACM Transactions on Sensor network (ToSN
, 2006
"... We study the problem of detecting and eliminating redundancy in a sensor network with a view to improving energy efficiency, while preserving the network’s coverage. We also examine the impact of redundancy elimination on the related problem of coverageboundary detection. We reduce both problems to ..."
Abstract

Cited by 20 (0 self)
 Add to MetaCart
We study the problem of detecting and eliminating redundancy in a sensor network with a view to improving energy efficiency, while preserving the network’s coverage. We also examine the impact of redundancy elimination on the related problem of coverageboundary detection. We reduce both problems to the computation of Voronoi diagrams, prove and achieve lower bounds on the solution of these problems, and present efficient distributed algorithms for computing and maintaining solutions in cases of sensor failures or insertion of new sensors. We prove the correctness and termination properties of our distributed algorithms, and analytically characterize the time complexity and traffic generated by our algorithms. Using detailed simulations, we also quantify the impact of system parameters such as sensor density, transmission range, and failure rates on network traffic.