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74,343
Dense Point Sets Have Sparse Delaunay Triangulations
"... Delaunay triangulations and Voronoi diagrams are one of the most thoroughly studies objects in computational geometry, with numerous applications including nearest-neighbor searching, clustering, finite-element mesh generation, deformable surface modeling, and surface reconstruction. Many algorithms ..."
Abstract
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Cited by 29 (2 self)
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algorithms in these application domains begin by constructing the Delaunay triangulation or Voronoi diagram of a set of points in R³. Since three-dimensional Delaunay triangulations can have complexity Ω(n²) in the worst case, these algorithms have worst-case running time \Omega (n2). However, this behavior
Dense Point Sets Have Sparse Delaunay Triangulations ∗ �� � ���� � �� � ��� � �� � ������ � ��������� or “... But Not Too Nasty”
, 2001
"... ..."
Dense Point Sets Have Sparse Delaunay Triangulations or "... But Not Too Nasty"
, 2003
"... The spread of a finite set of points is the ratio between the longest and shortest pairwise distances. We prove that the Delaunay triangulation of any set of n points in R³ with spread \Delta has complexity O(\Delta 3). This bound is tight in the worst case for all \Delta = O(pn). In particular, t ..."
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, the Delaunay triangulation of any dense point set has linear complexity. We also generalize this upper bound to regular triangulations of k-ply systems of balls, unions of several dense point sets, and uniform samples of smooth surfaces. On the other hand, for any n and \Delta = O(n), we construct a regular
Iterative point matching for registration of free-form curves and surfaces
, 1994
"... A heuristic method has been developed for registering two sets of 3-D curves obtained by using an edge-based stereo system, or two dense 3-D maps obtained by using a correlation-based stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in ma ..."
Abstract
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Cited by 660 (8 self)
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A heuristic method has been developed for registering two sets of 3-D curves obtained by using an edge-based stereo system, or two dense 3-D maps obtained by using a correlation-based stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms.
- In IEEE Workshop on Stereo and Multi-Baseline Vision,
, 2001
"... Abstract Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame ..."
Abstract
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Cited by 1546 (22 self)
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Abstract Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two
Interior-point Methods
, 2000
"... The modern era of interior-point methods dates to 1984, when Karmarkar proposed his algorithm for linear programming. In the years since then, algorithms and software for linear programming have become quite sophisticated, while extensions to more general classes of problems, such as convex quadrati ..."
Abstract
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Cited by 612 (15 self)
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The modern era of interior-point methods dates to 1984, when Karmarkar proposed his algorithm for linear programming. In the years since then, algorithms and software for linear programming have become quite sophisticated, while extensions to more general classes of problems, such as convex
Surface reconstruction from unorganized points
- COMPUTER GRAPHICS (SIGGRAPH ’92 PROCEEDINGS)
, 1992
"... We describe and demonstrate an algorithm that takes as input an unorganized set of points fx1�:::�xng IR 3 on or near an unknown manifold M, and produces as output a simplicial surface that approximates M. Neither the topology, the presence of boundaries, nor the geometry of M are assumed to be know ..."
Abstract
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Cited by 815 (8 self)
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We describe and demonstrate an algorithm that takes as input an unorganized set of points fx1�:::�xng IR 3 on or near an unknown manifold M, and produces as output a simplicial surface that approximates M. Neither the topology, the presence of boundaries, nor the geometry of M are assumed
An affine invariant interest point detector
- In Proceedings of the 7th European Conference on Computer Vision
, 2002
"... Abstract. This paper presents a novel approach for detecting affine invariant interest points. Our method can deal with significant affine transformations including large scale changes. Such transformations introduce significant changes in the point location as well as in the scale and the shape of ..."
Abstract
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Cited by 1467 (55 self)
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to affine invariant points. For matching and recognition, the image is characterized by a set of affine invariant points; the affine transformation associated with each point allows the computation of an affine invariant descriptor which is also invariant to affine illumination changes. A quantitative
Points-to Analysis in Almost Linear Time
, 1996
"... We present an interprocedural flow-insensitive points-to analysis based on type inference methods with an almost linear time cost complexity. To our knowledge, this is the asymptotically fastest non-trivial interprocedural points-to analysis algorithm yet described. The algorithm is based on a non-s ..."
Abstract
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Cited by 595 (3 self)
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-standard type system. The type inferred for any variable represents a set of locations and includes a type which in turn represents a set of locations possibly pointed to by the variable. The type inferred for a function variable represents a set of functions it may point to and includes a type signature
OPTICS: Ordering Points To Identify the Clustering Structure
, 1999
"... Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all of ..."
Abstract
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Cited by 527 (51 self)
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.g. representative points, arbitrary shaped clusters), but also the intrinsic clustering structure. For medium sized data sets, the cluster-ordering can be represented graphically and for very large data sets, we introduce an appropriate visualization technique. Both are suitable for interactive exploration
Results 1 - 10
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74,343
