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Random Sampling, Halfspace Range Reporting, and Construction of (≤k)Levels in Three Dimensions
 SIAM J. COMPUT
, 1999
"... Given n points in three dimensions, we show how to answer halfspace range reporting queries in O(logn+k) expected time for an output size k. Our data structure can be preprocessed in optimal O(n log n) expected time. We apply this result to obtain the first optimal randomized algorithm for the co ..."
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Cited by 38 (8 self)
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Given n points in three dimensions, we show how to answer halfspace range reporting queries in O(logn+k) expected time for an output size k. Our data structure can be preprocessed in optimal O(n log n) expected time. We apply this result to obtain the first optimal randomized algorithm for the construction of the ( k)level in an arrangement of n planes in three dimensions. The algorithm runs in O(n log n+nk²) expected time. Our techniques are based on random sampling. Applications in two dimensions include an improved data structure for "k nearest neighbors" queries, and an algorithm that constructs the orderk Voronoi diagram in O(n log n + nk log k) expected time.
Finding an optimal path without growing the tree
 6th Annual European Sym. on Algorithms, Springer LNCS
, 1998
"... For problems on computing an optimal path as well as its length in a certain setting, the “standard” approach for finding an actual optimal path is by building (or “growing”) a singlesource optimal path tree. In this paper, we study a class of optimal path problems with the following phenomenon: Th ..."
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Cited by 3 (0 self)
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For problems on computing an optimal path as well as its length in a certain setting, the “standard” approach for finding an actual optimal path is by building (or “growing”) a singlesource optimal path tree. In this paper, we study a class of optimal path problems with the following phenomenon: The space complexity of the algorithms for reporting the lengths of singlesource optimal paths for these problems is asymptotically smaller than the space complexity of the “standard ” treegrowing algorithms for finding actual optimal paths. We present a general and efficient algorithmic paradigm for finding an actual optimal path for such problems without having to grow a singlesource optimal path tree. Our paradigm is based on the “marriagebeforeconquer ” strategy, the pruneandsearch technique, and a new data structure called clipped trees. The paradigm enables us to compute an actual path for a number of optimal path problems and dynamic programming problems in computational geometry, graph theory, and combinatorial optimization. Our algorithmic
On the Volume and Resolution of 3Dimensional Convex Graph Drawing (Extended Abstract)
"... We address the problem of drawing a 3connected planar graph as a convex polyhedron in R³. We give an efficient algorithm for producing such a realization using O(n) volume under the vertexresolution rule. Each vertex in the drawing resulting from this method is guaranteed to need no more than O(n ..."
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We address the problem of drawing a 3connected planar graph as a convex polyhedron in R³. We give an efficient algorithm for producing such a realization using O(n) volume under the vertexresolution rule. Each vertex in the drawing resulting from this method is guaranteed to need no more than O(n log n) bits to represent (as a pair of rational numbers). This solves an open problem of Cohen, Eades, Lin, and Ruskey. We also show that under the angularresolution rule drawing a 3connected planar graph as a convex polyhedron in R³ requires at least exponential volume in the worst case.
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"... One of the most fundamental tasks in pattern recognition is to detect lines and curves from an image. It is often done after some appropriate edge detection process that results in a binary image in which pixels are classi ed into edge points (pixels) and nonedge points. Up to now a great number of ..."
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One of the most fundamental tasks in pattern recognition is to detect lines and curves from an image. It is often done after some appropriate edge detection process that results in a binary image in which pixels are classi ed into edge points (pixels) and nonedge points. Up to now a great number of algorithms have been proposed under the name of Hough Transform [7, 8].