Results 1  10
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14
Voronoi Diagrams of Moving Points
, 1995
"... Consider a set of n points in ddimensional Euclidean space, d 2, each of which is continuously moving along a given individual trajectory. At each instant in time, the points define a Voronoi diagram. As the points move, the Voronoi diagram changes continuously, but at certain critical instants in ..."
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Cited by 45 (6 self)
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Consider a set of n points in ddimensional Euclidean space, d 2, each of which is continuously moving along a given individual trajectory. At each instant in time, the points define a Voronoi diagram. As the points move, the Voronoi diagram changes continuously, but at certain critical instants in time, topological events occur that cause a change in the Voronoi diagram. In this paper, we present a method of maintaining the Voronoi diagram over time, while showing that the number of topological events has an upper bound of O(n d s (n)), where s (n) is the maximum length of a (n; s)DavenportSchinzel sequence [AgShSh 89, DaSc 65] and s is a constant depending on the motions of the point sites. Our results are a linearfactor improvement over the naive O(n d+2 ) upper bound on the number of topological events. In addition, we show that if only k points are moving (while leaving the other n \Gamma k points fixed), there is an upper bound of O(kn d\Gamma1 s (n) + (n \Gamma k)...
2D Euclidean distance transform algorithms: A comparative survey
 ACM COMPUTING SURVEYS
, 2008
"... The distance transform (DT) is a general operator forming the basis of many methods in computer vision and geometry, with great potential for practical applications. However, all the optimal algorithms for the computation of the exact Euclidean DT (EDT) were proposed only since the 1990s. In this wo ..."
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Cited by 37 (2 self)
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The distance transform (DT) is a general operator forming the basis of many methods in computer vision and geometry, with great potential for practical applications. However, all the optimal algorithms for the computation of the exact Euclidean DT (EDT) were proposed only since the 1990s. In this work, stateoftheart sequential 2D EDT algorithms are reviewed and compared, in an effort to reach more solid conclusions regarding their differences in speed and their exactness. Six of the best algorithms were fully implemented and compared in practice.
Shapes And Implementations In ThreeDimensional Geometry
, 1993
"... Frequently, data in scientific computing is in its abstract form a finite point set in space, and it is often useful or required to compute what one might call the "shape" of the set. For that purpose, this thesis deals with the formal notion of the family of alpha shapes of a finite point ..."
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Cited by 37 (5 self)
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Frequently, data in scientific computing is in its abstract form a finite point set in space, and it is often useful or required to compute what one might call the "shape" of the set. For that purpose, this thesis deals with the formal notion of the family of alpha shapes of a finite point set in three dimensional space. Each shape is a welldefined polytope, derived from the Delaunay triangulation of the point set, with a real parameter controlling the desired level of detail. Algorithms and data structures are presented that construct and store the entire family of shapes, with a quadratic time and space complexity, in the worst case.
Reasoning about Categories in Conceptual Spaces
 In Proceedings of the Fourteenth International Joint Conference of Artificial Intelligence
, 2001
"... Understanding the process of categorization is a primary research goal in artificial intelligence. The conceptual space framework provides a flexible approach to modeling contextsensitive categorization via a geometrical representation designed for modeling and managing concepts. In this paper we s ..."
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Cited by 16 (1 self)
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Understanding the process of categorization is a primary research goal in artificial intelligence. The conceptual space framework provides a flexible approach to modeling contextsensitive categorization via a geometrical representation designed for modeling and managing concepts. In this paper we show how algorithms developed in computational geometry, and the Region Connection Calculus can be used to model important aspects of categorization in conceptual spaces. In particular, we demonstrate the feasibility of using existing geometric algorithms to build and manage categories in conceptual spaces, and we show how the Region Connection Calculus can be used to reason about categories and other conceptual regions. 1
A Robust Implementation For ThreeDimensional Delaunay Triangulations
, 1995
"... This paper presents Detri 2.2, an implementation for Delaunay triangulations of threedimensional point sets. The code uses a variant of the randomized incrementalflip algorithm, and employs a symbolic perturbation scheme to achieve robustness. The algorithm's time complexity is quadratic in n ..."
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Cited by 11 (0 self)
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This paper presents Detri 2.2, an implementation for Delaunay triangulations of threedimensional point sets. The code uses a variant of the randomized incrementalflip algorithm, and employs a symbolic perturbation scheme to achieve robustness. The algorithm's time complexity is quadratic in n, the number of input points, and this is optimal in the worst case. However, empirically, we can expect running times roughly proportional to the number of triangles in the final triangulation, which typically is linear in n. Even though the symbolic perturbation scheme relies on exact arithmetic, the resulting code is efficient in practice. This is mainly due to a careful implementation of the geometric primitives and the arithmetic module. The source code is freely available on the Internet.
Convex Distance Functions in 3Space are Different
 Fundam. Inform
, 1994
"... The bisector systems of convex distance functions in 3space are investigated and it is shown that there is a substantial difference to the Euclidean metric which cannot be observed in 2space. This disproves the general belief that Voronoi diagrams in convex distance functions are, in any dimension ..."
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Cited by 10 (6 self)
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The bisector systems of convex distance functions in 3space are investigated and it is shown that there is a substantial difference to the Euclidean metric which cannot be observed in 2space. This disproves the general belief that Voronoi diagrams in convex distance functions are, in any dimension, analogous to Euclidean Voronoi diagrams. The fact is that more spheres than one can pass through four points in general position. In the L 4 metric, there exist quadrupels of points that lie on the surface of three L 4 spheres. Moreover, for each n # 0 one can construct a smooth and symmetric convex distance function d and four points that are contained in the surface of exactly 2n +1 dspheres, and this number does not decrease if the four points are disturbed independently within 3dimensional neighborhoods. This result implies that there is no general upper bound to the complexity of the Voronoi diagram of four sites based on a convex distance function in 3space.
"The Big Sweep": On the Power of the Wavefront Approach to Voronoi Diagrams
, 1992
"... We show that the wavefront approach to Voronoi diagrams (a deterministic line sweep algorithm that does not use geometric transform) can be generalized to distance measures more general than the Euclidean metric. In fact, we provide the first worstcase optimal (O(n log time, O(n) space) algo ..."
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Cited by 6 (2 self)
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We show that the wavefront approach to Voronoi diagrams (a deterministic line sweep algorithm that does not use geometric transform) can be generalized to distance measures more general than the Euclidean metric. In fact, we provide the first worstcase optimal (O(n log time, O(n) space) algorithm that is valid for the full class of what has been called nice metrics in the plane. This also solves the previously open problem of providing an time planesweep algorithm for arbitrary L k metrics.
OutputSensitive Construction Of Convex Hulls
, 1995
"... The construction of the convex hull of a finite point set in a lowdimensional Euclidean space is a fundamental problem in computational geometry. This thesis investigates efficient algorithms for the convex hull problem, where complexity is measured as a function of both the size of the input point ..."
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Cited by 3 (0 self)
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The construction of the convex hull of a finite point set in a lowdimensional Euclidean space is a fundamental problem in computational geometry. This thesis investigates efficient algorithms for the convex hull problem, where complexity is measured as a function of both the size of the input point set and the size of the output polytope. Two new, simple, optimal, outputsensitive algorithms are presented in two dimensions and a simple, optimal, outputsensitive algorithm is presented in three dimensions. In four dimensions, we give the first outputsensitive algorithm that is within a polylogarithmic factor of optimal. In higher fixed dimensions, we obtain an algorithm that is optimal for sufficiently small output sizes and is faster than previous methods for sublinear output sizes; this result is further improved in even dimensions. Although the focus of the thesis is on the convex hull problem, applications of our techniques to many related problems in computational geometry are al...
Maintaining Voronoi diagrams in parallel
 Proc. ACMIEEE Hawaii International Conference on System Sciences HICSS'94, Maui
, 1994
"... ..."
MONOTONICITY PRESERVING APPROXIMATION OF MULTIVARIATE SCATTERED DATA ∗
"... This paper describes a new method of monotone interpolation and smoothing of multivariate scattered data. It is based on the assumption that the function to be approximated is Lipschitz continuous. The method provides the optimal approximation in the worst case scenario and tight error bounds. Smoot ..."
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Cited by 1 (0 self)
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This paper describes a new method of monotone interpolation and smoothing of multivariate scattered data. It is based on the assumption that the function to be approximated is Lipschitz continuous. The method provides the optimal approximation in the worst case scenario and tight error bounds. Smoothing of noisy data subject to monotonicity constraints is converted into a quadratic programming problem. Estimation of the unknown Lipschitz constant from the data by sample splitting and crossvalidation is described. Extension of the method for locally Lipschitz functions is presented. AMS subject classification (2000): 41A29,65D05,41A15,65D10.