de Mt Mark Berg
Eindhoven University of Technology
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Featured researches published by de Mt Mark Berg.
Algorithmica | 1997
de Mt Mark Berg; van Mj Marc Kreveld
Abstract. Let F be a polyhedral terrain with n vertices. We show how to preprocess F such that for any two query points on F it can be decided whether there exists a path on F between the two points whose height decreases monotonically. More generally, the minimum total ascent or descent along any path between the two points can be computed. It is also possible to decide, given two query points and a height, whether there is a path that stays below this height. All these queries can be answered with one data structure which stores the so-called height-level map of the terrain. Although the height-level map has quadratic worst-case complexity, it is stored implicitly using only linear storage. The query time for all the above queries is
Algorithmica | 2000
de Mt Mark Berg
O(\log n)
Discrete and Computational Geometry | 1999
Boris Aronov; de Mt Mark Berg; van der Af Frank Stappen; Petr Svestka; Jules Vleugels
and the structure can be built in
Discrete and Computational Geometry | 1996
de Mt Mark Berg; Leonidas J. Guibas; Dan Halperin
O(n\log n)
Theory of Computing Systems \/ Mathematical Systems Theory | 1998
de Mt Mark Berg; O Otfried Cheong; Olivier Devillers; van Mj Marc Kreveld; Monique Teillaud
time. A path with the desired property can be reported in additional time that is linear in the description size of the path.Let F be a polyhedral terrain with n vertices. We show how to preprocess F such that for any two query points on F it can be decided whether there exists a path on F between the two points whose height decreases monotonically. More generally, the minimum total ascent or descent along any path between the two points can be computed. It is also possible to decide, given two query points and a height, whether there is a path that stays below this height. All these queries can be answered with one data structure which stores the so-called height-level map of the terrain. Although the height-level map has quadratic worst-case complexity, it is stored implicitly using only linear storage. The query time for all the above queries is
Discrete and Computational Geometry | 1998
van der Af Frank Stappen; Mark H. Overmars; de Mt Mark Berg; Jules Vleugels
O(\log n)
symposium on computational geometry | 1991
de Mt Mark Berg; Dan Halperin; Mark H. Overmars; J Jack Snoeyink; van Mj Marc Kreveld
and the structure can be built in
scandinavian workshop on algorithm theory | 1990
de Mt Mark Berg; van Mj Marc Kreveld; Bengt J. Nilsson; Mark H. Overmars
O(n\log n)
Discrete and Computational Geometry | 2010
Mohammad Ali Abam; de Mt Mark Berg; P Peter Hachenberger; Alireza Zarei
time. A path with the desired property can be reported in additional time that is linear in the description size of the path.
foundations of computer science | 1990
de Mt Mark Berg; Mark H. Overmars
We describe a new and simple method for constructing binary space partitions (BSPs) in arbitrary dimensions. We also introduce the concept of uncluttered scenes, which are scenes with a certain property that we suspect many realistic scenes exhibit, and we show that our method constructs a BSP of size O(n) for an uncluttered scene consisting of n objects. The construction time is O(n log n) . Because any set of disjoint fat objects is uncluttered, our result implies an efficient method to construct a linear size BSP for fat objects. We use our BSP to develop a data structure for point location in uncluttered scenes. The query time of our structure is O( log n) , and the amount of storage is O(n) . This result can in turn be used to perform range queries with not-too-small ranges in scenes consisting of disjoint fat objects or, more generally, in so-called low-density scenes.Abstract. We describe a new and simple method for constructing binary space partitions (BSPs) in arbitrary dimensions. We also introduce the concept of uncluttered scenes, which are scenes with a certain property that we suspect many realistic scenes exhibit, and we show that our method constructs a BSP of size O(n) for an uncluttered scene consisting of n objects. The construction time is O(n log n) . Because any set of disjoint fat objects is uncluttered, our result implies an efficient method to construct a linear size BSP for fat objects. We use our BSP to develop a data structure for point location in uncluttered scenes. The query time of our structure is O( log n) , and the amount of storage is O(n) . This result can in turn be used to perform range queries with not-too-small ranges in scenes consisting of disjoint fat objects or, more generally, in so-called low-density scenes.