250 research outputs found

    How to Walk Your Dog in the Mountains with No Magic Leash

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    We describe a O(logn)O(\log n )-approximation algorithm for computing the homotopic \Frechet distance between two polygonal curves that lie on the boundary of a triangulated topological disk. Prior to this work, algorithms were known only for curves on the Euclidean plane with polygonal obstacles. A key technical ingredient in our analysis is a O(logn)O(\log n)-approximation algorithm for computing the minimum height of a homotopy between two curves. No algorithms were previously known for approximating this parameter. Surprisingly, it is not even known if computing either the homotopic \Frechet distance, or the minimum height of a homotopy, is in NP

    Colored Non-Crossing Euclidean Steiner Forest

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    Given a set of kk-colored points in the plane, we consider the problem of finding kk trees such that each tree connects all points of one color class, no two trees cross, and the total edge length of the trees is minimized. For k=1k=1, this is the well-known Euclidean Steiner tree problem. For general kk, a kρk\rho-approximation algorithm is known, where ρ1.21\rho \le 1.21 is the Steiner ratio. We present a PTAS for k=2k=2, a (5/3+ε)(5/3+\varepsilon)-approximation algorithm for k=3k=3, and two approximation algorithms for general~kk, with ratios O(nlogk)O(\sqrt n \log k) and k+εk+\varepsilon

    MAP: Medial Axis Based Geometric Routing in Sensor Networks

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    One of the challenging tasks in the deployment of dense wireless networks (like sensor networks) is in devising a routing scheme for node to node communication. Important consideration includes scalability, routing complexity, the length of the communication paths and the load sharing of the routes. In this paper, we show that a compact and expressive abstraction of network connectivity by the medial axis enables efficient and localized routing. We propose MAP, a Medial Axis based naming and routing Protocol that does not require locations, makes routing decisions locally, and achieves good load balancing. In its preprocessing phase, MAP constructs the medial axis of the sensor field, defined as the set of nodes with at least two closest boundary nodes. The medial axis of the network captures both the complex geometry and non-trivial topology of the sensor field. It can be represented compactly by a graph whose size is comparable with the complexity of the geometric features (e.g., the number of holes). Each node is then given a name related to its position with respect to the medial axis. The routing scheme is derived through local decisions based on the names of the source and destination nodes and guarantees delivery with reasonable and natural routes. We show by both theoretical analysis and simulations that our medial axis based geometric routing scheme is scalable, produces short routes, achieves excellent load balancing, and is very robust to variations in the network model

    Geometric Multicut

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    We study the following separation problem: Given a collection of colored objects in the plane, compute a shortest "fence" F, i.e., a union of curves of minimum total length, that separates every two objects of different colors. Two objects are separated if F contains a simple closed curve that has one object in the interior and the other in the exterior. We refer to the problem as GEOMETRIC k-CUT, where k is the number of different colors, as it can be seen as a geometric analogue to the well-studied multicut problem on graphs. We first give an O(n^4 log^3 n)-time algorithm that computes an optimal fence for the case where the input consists of polygons of two colors and n corners in total. We then show that the problem is NP-hard for the case of three colors. Finally, we give a (2-4/3k)-approximation algorithm

    A Fixed Parameter Tractable Approximation Scheme for the Optimal Cut Graph of a Surface

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    Given a graph GG cellularly embedded on a surface Σ\Sigma of genus gg, a cut graph is a subgraph of GG such that cutting Σ\Sigma along GG yields a topological disk. We provide a fixed parameter tractable approximation scheme for the problem of computing the shortest cut graph, that is, for any ε>0\varepsilon >0, we show how to compute a (1+ε)(1+ \varepsilon) approximation of the shortest cut graph in time f(ε,g)n3f(\varepsilon, g)n^3. Our techniques first rely on the computation of a spanner for the problem using the technique of brick decompositions, to reduce the problem to the case of bounded tree-width. Then, to solve the bounded tree-width case, we introduce a variant of the surface-cut decomposition of Ru\'e, Sau and Thilikos, which may be of independent interest

    Geometric Multicut

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    We study the following separation problem: Given a collection of colored objects in the plane, compute a shortest "fence" FF, i.e., a union of curves of minimum total length, that separates every two objects of different colors. Two objects are separated if FF contains a simple closed curve that has one object in the interior and the other in the exterior. We refer to the problem as GEOMETRIC kk-CUT, where kk is the number of different colors, as it can be seen as a geometric analogue to the well-studied multicut problem on graphs. We first give an O(n4log3n)O(n^4\log^3 n)-time algorithm that computes an optimal fence for the case where the input consists of polygons of two colors and nn corners in total. We then show that the problem is NP-hard for the case of three colors. Finally, we give a (24/3k)(2-4/3k)-approximation algorithm.Comment: 24 pages, 15 figure

    Robust semi-automated path extraction for visualising stenosis of the coronary arteries

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    Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3-D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets
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