Computational Geometry: Proximity and Location
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Medial Axis Transform Using Ridge Following
Missouri University of Science and Technology Scholars' Mine Computer Science Technical Reports Computer Science 01 Aug 1987 Medial Axis Transform using Ridge Following Richard Mark Volkmann Daniel C. St. Clair Missouri University of Science and Technology Follow this and additional works at: https://scholarsmine.mst.edu/comsci_techreports Part of the Computer Sciences Commons Recommended Citation Volkmann, Richard Mark and St. Clair, Daniel C., "Medial Axis Transform using Ridge Following" (1987). Computer Science Technical Reports. 80. https://scholarsmine.mst.edu/comsci_techreports/80 This Technical Report is brought to you for free and open access by Scholars' Mine. It has been accepted for inclusion in Computer Science Technical Reports by an authorized administrator of Scholars' Mine. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [email protected]. MEDIAL AXIS TRANSFORM USING RIDGE FOLLOWING R. M. Volkmann* and D. C. St. Clair CSc 87-17 *This report is substantially the M.S. thesis of the first author, completed, August 1987 Page iii ABSTRACT The intent of this investigation has been to find a robust algorithm for generation of the medial axis transform (MAT). The MAT is an invertible, object centered, shape representation defined as the collection of the centers of disks contained in the shape but not in any other such disk. Its uses include feature extraction, shape smoothing, and data compression. MAT generating algorithms include brushfire, Voronoi diagrams, and ridge following. An improved implementation of the ridge following algorithm is given. Orders of the MAT generating algorithms are compared. -
Computing 2D Periodic Centroidal Voronoi Tessellation Dong-Ming Yan, Kai Wang, Bruno Lévy, Laurent Alonso
Computing 2D Periodic Centroidal Voronoi Tessellation Dong-Ming Yan, Kai Wang, Bruno Lévy, Laurent Alonso To cite this version: Dong-Ming Yan, Kai Wang, Bruno Lévy, Laurent Alonso. Computing 2D Periodic Centroidal Voronoi Tessellation. 8th International Symposium on Voronoi Diagrams in Science and Engineering - ISVD2011, Jun 2011, Qingdao, China. 10.1109/ISVD.2011.31. inria-00605927 HAL Id: inria-00605927 https://hal.inria.fr/inria-00605927 Submitted on 5 Jul 2011 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Computing 2D Periodic Centroidal Voronoi Tessellation Dong-Ming Yan Kai Wang Bruno Levy´ Laurent Alonso Project ALICE, INRIA Project ALICE, INRIA Project ALICE, INRIA Project ALICE, INRIA Nancy, France / Gipsa-lab, CNRS Nancy, France Nancy, France [email protected] Grenoble, France [email protected] [email protected] [email protected] Abstract—In this paper, we propose an efficient algorithm to compute the centroidal Voronoi tessellation in 2D periodic space. We first present a simple algorithm for constructing the periodic Voronoi diagram (PVD) from a Euclidean Voronoi diagram. The presented PVD algorithm considers only a small set of periodic copies of the input sites, which is more efficient than previous approaches requiring full copies of the sites (9 in 2D and 27 in 3D). -
Automatic Generation of Smooth Paths Bounded by Polygonal Chains
Automatic Generation of Smooth Paths Bounded by Polygonal Chains T. Berglund1, U. Erikson1, H. Jonsson1, K. Mrozek2, and I. S¨oderkvist3 1 Department of Computer Science and Electrical Engineering, Lule˚a University of Technology, SE-971 87 Lule˚a, Sweden {Tomas.Berglund,Ulf.Erikson,Hakan.Jonsson}@sm.luth.se 2 Q Navigator AB, SE-971 75 Lule˚a, Sweden [email protected] 3 Department of Mathematics, Lule˚a University of Technology, SE-971 87 Lule˚a, Sweden [email protected] Abstract We consider the problem of planning smooth paths for a vehicle in a region bounded by polygonal chains. The paths are represented as B-spline functions. A path is found by solving an optimization problem using a cost function designed to care for both the smoothness of the path and the safety of the vehicle. Smoothness is defined as small magnitude of the derivative of curvature and safety is defined as the degree of centering of the path between the polygonal chains. The polygonal chains are preprocessed in order to remove excess parts and introduce safety margins for the vehicle. The method has been implemented for use with a standard solver and tests have been made on application data provided by the Swedish mining company LKAB. 1 Introduction We study the problem of planning a smooth path between two points in a planar map described by polygonal chains. Here, a smooth path is a curve, with continuous derivative of curvature, that is a solution to a certain optimization problem. Today, it is not known how to find a closed form of an optimal path. -
Voronoi Diagrams--A Survey of a Fundamental Geometric Data Structure
Voronoi Diagrams — A Survey of a Fundamental Geometric Data Structure FRANZ AURENHAMMER Institute fur Informationsverarbeitung Technische Universitat Graz, Sch iet!stattgasse 4a, Austria This paper presents a survey of the Voronoi diagram, one of the most fundamental data structures in computational geometry. It demonstrates the importance and usefulness of the Voronoi diagram in a wide variety of fields inside and outside computer science and surveys the history of its development. The paper puts particular emphasis on the unified exposition of its mathematical and algorithmic properties. Finally, the paper provides the first comprehensive bibliography on Voronoi diagrams and related structures. Categories and Subject Descriptors: F.2.2 [Analysis of Algorithms and Problem Complexity]: Nonnumerical Algorithms and Problems–geometrical problems and computations; G. 2.1 [Discrete Mathematics]: Combinatorics— combinatorial algorithms; I. 3.5 [Computer Graphics]: Computational Geometry and Object Modeling—geometric algorithms, languages, and systems General Terms: Algorithms, Theory Additional Key Words and Phrases: Cell complex, clustering, combinatorial complexity, convex hull, crystal structure, divide-and-conquer, geometric data structure, growth model, higher dimensional embedding, hyperplane arrangement, k-set, motion planning, neighbor searching, object modeling, plane-sweep, proximity, randomized insertion, spanning tree, triangulation INTRODUCTION [19841 and to the textbooks by Preparata and Shames [1985] and Edelsbrunner Computational geometry is concerned [1987bl.) with the design and analysis of algo- Readers familiar with the literature of rithms for geometrical problems. In add- computational geometry will have no- ition, other more practically oriented, ticed, especially in the last few years, an areas of computer science— such as com- increasing interest in a geometrical con- puter graphics, computer-aided design, struct called the Voronoi diagram. -
Voronoi Diagram of Polygonal Chains Under the Discrete Frechet Distance
Voronoi Diagram of Polygonal Chains under the Discrete Fr´echet Distance Sergey Bereg1, Kevin Buchin2,MaikeBuchin2, Marina Gavrilova3, and Binhai Zhu4 1 Department of Computer Science, University of Texas at Dallas, Richardson, TX 75083, USA [email protected] 2 Department of Information and Computing Sciences, Universiteit Utrecht, The Netherlands {buchin,maike}@cs.uu.nl 3 Department of Computer Science, University of Calgary, Calgary, Alberta T2N 1N4, Canada [email protected] 4 Department of Computer Science, Montana State University, Bozeman, MT 59717-3880, USA [email protected] Abstract. Polygonal chains are fundamental objects in many applica- tions like pattern recognition and protein structure alignment. A well- known measure to characterize the similarity of two polygonal chains is the (continuous/discrete) Fr´echet distance. In this paper, for the first time, we consider the Voronoi diagram of polygonal chains in d-dimension under the discrete Fr´echet distance. Given a set C of n polygonal chains in d-dimension, each with at most k vertices, we prove fundamental proper- ties of such a Voronoi diagram VDF (C). Our main results are summarized as follows. dk+ – The combinatorial complexity of VDF (C)isatmostO(n ). dk – The combinatorial complexity of VDF (C)isatleastΩ(n )fordi- mension d =1, 2; and Ω(nd(k−1)+2) for dimension d>2. 1 Introduction The Fr´echet distance was first defined by Maurice Fr´echet in 1906 [8]. While known as a famous distance measure in the field of mathematics (more specifi- cally, abstract spaces), it was first applied in measuring the similarity of polygo- nal curves by Alt and Godau in 1992 [2]. -
Planar Delaunay Triangulations and Proximity Structures
Wolfgang Mulzer Institut für Informatik Planar Delaunay Triangulations and Proximity Structures Proximity Structures Given: a set P of n points in the plane proximity structure: a structure that “encodes useful information about the local relationships of the points in P” Planar Delaunay Triangulations and Proximity Structures 2 Proximity Structures Given: a set P of n points in the plane proximity structure: a structure that “encodes useful information about the local relationships of the points in P” Planar Delaunay Triangulations and Proximity Structures 3 Proximity Structures Given: a set P of n points in the plane proximity structure: a structure that “encodes useful information about the local relationships of the points in P” Planar Delaunay Triangulations and Proximity Structures 4 Proximity Structures Reduction from sorting → Ω usually need (n log n) to build a proximity structure Planar Delaunay Triangulations and Proximity Structures 5 Proximity Structures But: shouldn’t one proximity structure suffice to construct another proximity structure faster? Voronoi diagram → Quadtree s s s s s Planar Delaunay Triangulations and Proximity Structures 6 Proximity Structures Point sets may exhibit strange behaviors, so this is not always easy. Planar Delaunay Triangulations and Proximity Structures 7 Proximity Structures There might be clusters… Planar Delaunay Triangulations and Proximity Structures 8 Proximity Structures …high degrees… Planar Delaunay Triangulations and Proximity Structures 9 Proximity Structures or large spread. Planar -
Separating a Voronoi Diagram Via Local Search
Separating a Voronoi Diagram via Local Search Vijay V. S. P. Bhattiprolu1 and Sariel Har-Peled∗2 1 School of Computer Science, Carnegie Mellon University, Pittsburgh, USA [email protected] 2 Department of Computer Science, University of Illinois, Urbana, USA [email protected] Abstract d 1−1/d Given a set P of n points in R , we show how to insert a set Z of O n additional points, such that P can be broken into two sets P1 and P2, of roughly equal size, such that in the Voronoi diagram V(P ∪ Z), the cells of P1 do not touch the cells of P2; that is, Z separates P1 from P2 in the Voronoi diagram (and also in the dual Delaunay triangulation). In addition, given such a partition (P1,P2) of P , we present an approximation algorithm to compute a minimum size separator realizing this partition. We also present a simple local search algorithm that is a PTAS for approximating the optimal Voronoi partition. 1998 ACM Subject Classification F.2.2 Nonnumerical Algorithms and Problems, I.1.2 Algo- rithms, I.3.5 Computational Geometry and Object Modeling Keywords and phrases Separators, Local search, Approximation, Voronoi diagrams, Delaunay triangulation, Meshing, Geometric hitting set Digital Object Identifier 10.4230/LIPIcs.SoCG.2016.18 1 Introduction Many algorithms work by partitioning the input into a small number of pieces, of roughly equal size, with little interaction between the different pieces, and then recurse on these pieces. One natural way to compute such partitions for graphs is via the usage of separators. -
Two Algorithms for Nearest-Neighbor Search in High Dimensions
Two Algorithms for Nearest-Neighbor Search in High Dimensions Jon M. Kleinberg∗ February 7, 1997 Abstract Representing data as points in a high-dimensional space, so as to use geometric methods for indexing, is an algorithmic technique with a wide array of uses. It is central to a number of areas such as information retrieval, pattern recognition, and statistical data analysis; many of the problems arising in these applications can involve several hundred or several thousand dimensions. We consider the nearest-neighbor problem for d-dimensional Euclidean space: we wish to pre-process a database of n points so that given a query point, one can ef- ficiently determine its nearest neighbors in the database. There is a large literature on algorithms for this problem, in both the exact and approximate cases. The more sophisticated algorithms typically achieve a query time that is logarithmic in n at the expense of an exponential dependence on the dimension d; indeed, even the average- case analysis of heuristics such as k-d trees reveals an exponential dependence on d in the query time. In this work, we develop a new approach to the nearest-neighbor problem, based on a method for combining randomly chosen one-dimensional projections of the underlying point set. From this, we obtain the following two results. (i) An algorithm for finding ε-approximate nearest neighbors with a query time of O((d log2 d)(d + log n)). (ii) An ε-approximate nearest-neighbor algorithm with near-linear storage and a query time that improves asymptotically on linear search in all dimensions. -
28 ARRANGEMENTS Dan Halperin and Micha Sharir
28 ARRANGEMENTS Dan Halperin and Micha Sharir INTRODUCTION Given a finite collection of geometric objects such as hyperplanes or spheres in Rd, the arrangement ( S) is the decomposition of Rd into connected open cells of dimensions 0, 1,...,d AinducedS by . Besides being interesting in their own right, ar- rangements of hyperplanes have servedS as a unifying structure for many problems in discrete and computational geometry. With the recent advances in the study of arrangements of curved (algebraic) surfaces, arrangements have emerged as the underlying structure of geometric problems in a variety of “physical world” applica- tion domains such as robot motion planning and computer vision. This chapter is devoted to arrangements of hyperplanes and of curved surfaces in low-dimensional Euclidean space, with an emphasis on combinatorics and algorithms. In the first section we introduce basic terminology and combinatorics of ar- rangements. In Section 28.2 we describe substructures in arrangements and their combinatorial complexity. Section 28.3 deals with data structures for representing arrangements and with special refinements of arrangements. The following two sections focus on algorithms: algorithms for constructing full arrangements are described in Section 28.4, and algorithms for constructing substructures in Sec- tion 28.5. In Section 28.6 we discuss the relation between arrangements and other structures. Several applications of arrangements are reviewed in Section 28.7. Sec- tion 28.8 deals with robustness issues when implementing algorithms and data structures for arrangements and Section 28.9 surveys software implementations. We conclude in Section 28.10 with a brief review of Davenport-Schinzel sequences, a combinatorial structure that plays an important role in the analysis of arrange- ments. -
Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions
Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions Parikshit Ram, Dongryeol Lee, Hua Ouyang and Alexander G. Gray Computational Science and Engineering, Georgia Institute of Technology Atlanta, GA 30332 p.ram@,dongryel@cc.,houyang@,agray@cc. gatech.edu { } Abstract The long-standing problem of efficient nearest-neighbor (NN) search has ubiqui- tous applications ranging from astrophysics to MP3 fingerprinting to bioinformat- ics to movie recommendations. As the dimensionality of the dataset increases, ex- act NN search becomes computationally prohibitive; (1+) distance-approximate NN search can provide large speedups but risks losing the meaning of NN search present in the ranks (ordering) of the distances. This paper presents a simple, practical algorithm allowing the user to, for the first time, directly control the true accuracy of NN search (in terms of ranks) while still achieving the large speedups over exact NN. Experiments on high-dimensional datasets show that our algorithm often achieves faster and more accurate results than the best-known distance-approximate method, with much more stable behavior. 1 Introduction In this paper, we address the problem of nearest-neighbor (NN) search in large datasets of high dimensionality. It is used for classification (k-NN classifier [1]), categorizing a test point on the ba- sis of the classes in its close neighborhood. Non-parametric density estimation uses NN algorithms when the bandwidth at any point depends on the ktℎ NN distance (NN kernel density estimation [2]). NN algorithms are present in and often the main cost of most non-linear dimensionality reduction techniques (manifold learning [3, 4]) to obtain the neighborhood of every point which is then pre- served during the dimension reduction. -
Nearest Neighbor Search in Google Correlate
Nearest Neighbor Search in Google Correlate Dan Vanderkam Robert Schonberger Henry Rowley Google Inc Google Inc Google Inc 76 9th Avenue 76 9th Avenue 1600 Amphitheatre Parkway New York, New York 10011 New York, New York 10011 Mountain View, California USA USA 94043 USA [email protected] [email protected] [email protected] Sanjiv Kumar Google Inc 76 9th Avenue New York, New York 10011 USA [email protected] ABSTRACT queries are then noted, and a model that estimates influenza This paper presents the algorithms which power Google Cor- incidence based on present query data is created. relate[8], a tool which finds web search terms whose popu- This estimate is valuable because the most recent tradi- larity over time best matches a user-provided time series. tional estimate of the ILI rate is only available after a two Correlate was developed to generalize the query-based mod- week delay. Indeed, there are many fields where estimating eling techniques pioneered by Google Flu Trends and make the present[1] is important. Other health issues and indica- them available to end users. tors in economics, such as unemployment, can also benefit Correlate searches across millions of candidate query time from estimates produced using the techniques developed for series to find the best matches, returning results in less than Google Flu Trends. 200 milliseconds. Its feature set and requirements present Google Flu Trends relies on a multi-hour batch process unique challenges for Approximate Nearest Neighbor (ANN) to find queries that correlate to the ILI time series. Google search techniques. In this paper, we present Asymmetric Correlate allows users to create their own versions of Flu Hashing (AH), the technique used by Correlate, and show Trends in real time. -
R-Trees with Voronoi Diagrams for Efficient Processing of Spatial
VoR-Tree: R-trees with Voronoi Diagrams for Efficient Processing of Spatial Nearest Neighbor Queries∗ y Mehdi Sharifzadeh Cyrus Shahabi Google University of Southern California [email protected] [email protected] ABSTRACT A very important class of spatial queries consists of nearest- An important class of queries, especially in the geospatial neighbor (NN) query and its variations. Many studies in domain, is the class of nearest neighbor (NN) queries. These the past decade utilize R-trees as their underlying index queries search for data objects that minimize a distance- structures to address NN queries efficiently. The general ap- based function with reference to one or more query objects. proach is to use R-tree in two phases. First, R-tree's hierar- Examples are k Nearest Neighbor (kNN) [13, 5, 7], Reverse chical structure is used to quickly arrive to the neighborhood k Nearest Neighbor (RkNN) [8, 15, 16], k Aggregate Nearest of the result set. Second, the R-tree nodes intersecting with Neighbor (kANN) [12] and skyline queries [1, 11, 14]. The the local neighborhood (Search Region) of an initial answer applications of NN queries are numerous in geospatial deci- are investigated to find all the members of the result set. sion making, location-based services, and sensor networks. While R-trees are very efficient for the first phase, they usu- The introduction of R-trees [3] (and their extensions) for ally result in the unnecessary investigation of many nodes indexing multi-dimensional data marked a new era in de- that none or only a small subset of their including points veloping novel R-tree-based algorithms for various forms belongs to the actual result set.