Elliptic Gabriel graph for finding neighbors in a point set and its application to normal vector estimation

Point-based shape representation has received increased attention in recent years, mainly due to its simplicity. One of the most fundamental operations for point set processing is to find the neighbors of each point. Mesh structures and neighborhood graphs are commonly used for this purpose. However, though meshes are very popular in the field of computer graphics, neighbor relations encoded in a mesh are often distorted. Likewise, neighborhood graphs, such as the minimum spanning tree (MST), relative neighborhood graph (RNG), and Gabriel graph (GG), are also imperfect as they usually give too few neighbors for a given point. In this paper, we introduce a generalization of Gabriel graph, named elliptic Gabriel graph (EGG), which takes an elliptic influence region instead of the circular region in GG. In order to determine the appropriate aspect ratio of the elliptic influence region of EGG, this paper also presents the analysis between the aspect ratio of the elliptic influence region and the average valence of the resulting neighborhood. Analytic and empirical test results are included.     

Relative neighborhood graphs in the Li-metric

The relative neighborhood graph of a set of n points in the plane under the L₁-metric is considered. An algorithm that runs in O(nlog n) time for constructing the relative neighborhood graph based on the Delaunay triangulation is presented, improving a previously known algorithm that runs in O(n²log n) time.     

An optimal visibility graph algorithm for triangulated simple polygons

LetP be a triangulated simple polygon withn sides. The visibility graph ofP has an edge between every pair of polygon vertices that can be connected by an open segment in the interior ofP. We describe an algorithm that finds the visibility graph ofP inO(m) time, wherem is the number of edges in the visibility graph. Becausem can be as small asO(n), the algorithm improves on the more general visibility algorithms of Asanoet al. [AAGHI] and Welzl [W], which take Θ(n ²) time, and on Suri'sO(m logn) visibility graph algorithm for simple polygons [S].     

An optimal visibility graph algorithm for triangulated simple polygons

LetP be a triangulated simple polygon withn sides. The visibility graph ofP has an edge between every pair of polygon vertices that can be connected by an open segment in the interior ofP. We describe an algorithm that finds the visibility graph ofP inO(m) time, wherem is the number of edges in the visibility graph. Becausem can be as small asO(n), the algorithm improves on the more general visibility algorithms of Asanoet al. [AAGHI] and Welzl [W], which take (n ²) time, and on Suri'sO(m logn) visibility graph algorithm for simple polygons [S].This work was supported in part by a U.S. Army Research Office fellowship under agreement DAAG29-83-G-0020.     

A parallel algorithm for surface-based object reconstruction

This paper presents a parallel algorithm that approximates the surface of an object from a collection of its planar contours. Such a reconstruction has wide applications in such diverse fields as biological research, medical diagnosis and therapy, architecture, automobile and ship design, and solid modeling. The surface reconstruction problem is transformed into the problem of finding a minimum-cost acceptable path on a toroidal grid graph, where each horizontal and each vertical edge have the same orientation. An acceptable path is closed path that makes a complete horizontal and vertical circuit. We exploit the structure of this graph to develop efficient parallel algorithms for a message-passing computer. Givenp processors and anm byn toroidal graph, our algorithm will find the minimum cost acceptable path inO(mn log(m)/p) steps, ifp =O(mn/((m + n) log(mn/(m + n)))), which is an optimal speedup. We also show that the algorithm will sendO(p ²(m + n)) messages. The algorithm has a linear topology, so it is easy to embed the algorithm in common multiprocessor architectures.     

Bandwidth on AT-free graphs

We study the classical Bandwidth problem from the viewpoint of parametrised algorithms. Given a graph G=(V,E) and a positive integer k, the Bandwidth problem asks whether there exists a bijective function β:{1,…,∣V∣}→V such that for every edge uv∈E, ∣β⁻¹(u)−β⁻¹(v)∣≤k. It is known that under standard complexity assumptions, no algorithm for Bandwidth with running time of the form f(k)n^O(1) exists, even when the input is restricted to trees. We initiate the search for classes of graphs where such algorithms do exist. We present an algorithm with running time n⋅2^O(klogk) for Bandwidth on AT-free graphs, a well-studied graph class that contains interval, permutation, and cocomparability graphs. Our result is the first non-trivial algorithm that shows fixed-parameter tractability of Bandwidth on a graph class on which the problem remains NP-complete.     

An output sensitive algorithm for computing a maximum independent set of a circle graph

We present an output sensitive algorithm for computing a maximum independent set of an unweighted circle graph. Our algorithm requires O(nmin{d,α}) time at worst, for an n vertex circle graph where α is the independence number of the circle graph and d is its density. Previous algorithms for this problem required Θ(nd) time at worst.     

Minimum cycle bases of weighted outerplanar graphs

We give the first optimal algorithm that computes a minimum cycle basis for any weighted outerplanar graph. Specifically, for any n-node edge-weighted outerplanar graph G, we give an O(n)-time algorithm to obtain an O(n)-space compact representation Z(C) for a minimum cycle basis C of G. Each cycle in C can be computed from Z(C) in O(1) time per edge. Our result works for directed and undirected outerplanar graphs G.     

Using euler partitions to edge color bipartite multigraphs

An algorithm for finding a minimal edge coloring of a bipartite multigraph is presented. The algorithm usesO(V ^1/2 ElogV + V) time andO(E + V) space. It is based on a divide-and-conquer strategy, using euler partitions to divide the graph. A modification of the algorithm for matching is described. This algorithm finds a maximum matching of a regular bipartite graph with all degrees 2ⁿ, inO(E + V) time andO(E + V) space.This work was partially supported by the National Science Foundation under Grant GJ36461.     

Efficient Parallel Algorithms for Some Graph Theory Problems

In this paper,a sequential algorithm computing the aww vertex pair distance matrix D and the path matrix Pis given.On a PRAM EREW model with p,1≤p≤n^2,processors,a parallel version of the sequential algorithm is shown.This method can also be used to get a parallel algorithm to compute transitive closure array A^* of an undirected graph.The time complexity of the parallel algorithm is O(n^3/p).If D,P and A^* are known,it is shown that the problems to find all connected components,to compute the diameter of an undirected graph,to determine the center of a directed graph and to search for a directed cycle with the minimum(maximum)length in a directed graph can all be solved in O(n^2/p logp)time.     

Algorithms for updating minimal spanning trees

The problem of finding the minimal spanning tree on an undirected weighted graph has been investigated by many people and O(n²) algorithms are well known. P. M. Spira and A. Pan (Siam J. Computing4 (1975), 375–380) present an O(n) algorithm for updating the minimal spanning tree if a new vertex is inserted into the graph. In this paper, we present another O(n) algorithm simpler than that presented by Spira and Pan for the insertion of a vertex. Spira and Pan further show that the deletion of a vertex requires O(n²) steps. If all the vertices are considered, O(n³) steps may be used. The algorithm which we present here takes only O(n²) steps and labels the vertices of the graph in such a way that any vertex may be deleted from the graph and the minimal spanning tree can be updated in constant time. Similar results are obtained for the insertion and the deletion of an edge.     

A Fully Dynamic Graph Algorithm for Recognizing Interval Graphs

We present the first dynamic graph algorithm for recognizing interval graphs. The algorithm runs in O(nlog?n) worst-case time per edge deletion or edge insertion, where n is the number of vertices in the graph. The algorithm uses a new representation of interval graphs called the train tree, which is based on the clique-separator graph representation of chordal graphs. The train tree has a number of useful properties and it can be constructed from the clique-separator graph in O(n) time.     

Algorithms for computing diffuse reflection paths in polygons

Let s be a point source of light inside a polygon P of n vertices. A polygonal path from s to some point t inside P is called a diffuse reflection path if the turning points of the path lie on edges of?P. A?diffuse reflection path is said to be optimal if it has the minimum number of reflections on the path. The problem of computing a diffuse reflection path from s to t inside P has not been considered explicitly in the past. We present three different algorithms for this problem which produce suboptimal paths. For constructing such a path, the first algorithm uses a greedy method, the second algorithm uses a transformation of a minimum link path, and the third algorithm uses the edge–edge visibility graph of?P. The first two algorithms are for polygons without holes, and they run in O(n+klogn) time, where k denotes the number of reflections in the constructed path. The third algorithm is for polygons with or without holes, and it runs in O(n ²) time. The number of reflections in the path produced by this third algorithm can be at most three times that of an optimal diffuse reflection path. Though the combinatorial approach used in the third algorithm gives a better bound on the number of reflections on the path, the first and the second algorithms stand on the merit of their elegant geometric approaches based on local geometric information.     

A simple algorithm to find Hamiltonian cycles in proper interval graphs

We present an algorithm to find a Hamiltonian cycle in a proper interval graph in O(m+n) time, where m is the number of edges and n is the number of vertices in the graph. The algorithm is simpler and shorter than previous algorithms for the problem.     

AnO(n logn) plane-sweep algorithm forL 1 andL ∞ Delaunay triangulations

TheDelaunay diagram on a set of points in the plane, calledsites, is the straight-line dual graph of the Voronoi diagram. When no degeneracies are present, the Delaunay diagram is a triangulation of the sites, called theDelaunay triangulation. When degeneracies are present, edges must be added to the Delaunay diagram to obtain a Delaunay triangulation. In this paper we describe an optimalO(n logn) plane-sweep algorithm for computing a Delaunay triangulation on a possibly degenerate set of sites in the plane under theL ₁ metric or theL _∞ metric.     

Fast computation of smallest enclosing circle with center on a query line segment

Here we propose an efficient algorithm for computing the smallest enclosing circle whose center is constrained to lie on a query line segment. Our algorithm preprocesses a given set of n points P={p₁,p₂,…,pn} such that for any query line or line segment L, it efficiently locates a point c on L that minimizes the maximum distance among the points in P from c. Roy et al. [S. Roy, A. Karmakar, S. Das, S.C. Nandy, Constrained minimum enclosing circle with center on a query line segment, in: Proc. of the 31st Mathematical Foundation of Computer Science, 2006, pp. 765-776] have proposed an algorithm that solves the query problem in O(log²n) time using O(nlogn) preprocessing time and O(n) space. Our algorithm improves the query time to O(logn); but the preprocessing time and space complexities are both O(n²).     

Improved fixed parameter tractable algorithms for two “edge” problems: MAXCUT and MAXDAG

We give improved parameterized algorithms for two “edge” problems MAXCUT and MAXDAG, where the solution sought is a subset of edges. MAXCUT of a graph is a maximum set of edges forming a bipartite subgraph of the given graph. On the other hand, MAXDAG of a directed graph is a set of arcs of maximum size such that the graph induced on these arcs is acyclic. Our algorithms are obtained through new kernelization and efficient exact algorithms for the optimization versions of the problems. More precisely our results include:

(i)

a kernel with at most αk vertices and βk edges for MAXCUT. Here 0<α?1 and 1<β?2. Values of α and β depends on the number of vertices and the edges in the graph;

(ii)

a kernel with at most 4k/3 vertices and 2k edges for MAXDAG;

(iii)

an O^∗(k^1.2418) parameterized algorithm for MAXCUT in undirected graphs. This improves the O^∗(k^1.4143)¹ algorithm presented in [E. Prieto, The method of extremal structure on the k-maximum cut problem, in: The Proceedings of Computing: The Australasian Theory Symposium (CATS), 2005, pp. 119-126];

(iv)

an O^∗(n²) algorithm for optimization version of MAXDAG in directed graphs. This is the first such algorithm to the best of our knowledge;

(v)

an O^∗(k²) parameterized algorithm for MAXDAG in directed graphs. This improves the previous best of O^∗(k⁴) presented in [V. Raman, S. Saurabh, Parameterized algorithms for feedback set problems and their duals in tournaments, Theoretical Computer Science 351 (3) (2006) 446-458];

(vi)

an O^∗(k¹⁶) parameterized algorithm to determine whether an oriented graph having m arcs has an acyclic subgraph with at least m/2+k arcs. This improves the O^∗(k²) algorithm given in [V. Raman, S. Saurabh, Parameterized algorithms for feedback set problems and their duals in tournaments, Theoretical Computer Science 351 (3) (2006) 446-458].

In addition, we show that if a directed graph has minimum out degree at least f(n) (some function of n) then Directed Feedback Arc Set problem is fixed parameter tractable. The parameterized complexity of Directed Feedback Arc Set is a well-known open problem.     

Maintaining bridge-connected and biconnected components on-line

We consider the twin problems of maintaining the bridge-connected components and the biconnected components of a dynamic undirected graph. The allowed changes to the graph are vertex and edge insertions. We give an algorithm for each problem. With simple data structures, each algorithm runs inO(n logn +m) time, wheren is the number of vertices andm is the number of operations. We develop a modified version of the dynamic trees of Sleator and Tarjan that is suitable for efficient recursive algorithms, and use it to reduce the running time of the algorithms for both problems toO(mα(m,n)), where α is a functional inverse of Ackermann's function. This time bound is optimal. All of the algorithms useO(n) space.     

An algorithm for geometric minimum spanning trees requiring nearly linear expected time

We describe an algorithm for finding a minimum spanning tree of the weighted complete graph induced by a set ofn points in Euclideand-space. The algorithm requires nearly linear expected time for points that are independently uniformly distributed in the unitd-cube. The first step of the algorithm is the spiral search procedure described by Bentleyet al. [BWY82] for finding a supergraph of the MST that hasO(n) edges. (The constant factor in the bound depends ond.) The next step is that of sorting the edges of the supergraph by weight using a radix distribution, or “bucket,” sort. These steps require linear expected time. Finally, Kruskal's algorithm is used with the sorted edges, requiringO(nα(cn, n)) time in the worst case, withc>6. Since the function α(cn, n) grows very slowly, this step requires linear time for all practical purposes. This result improves the previous bestO(n log log*n), and employs a much simpler algorithm. Also, this result demonstrates the robustness of bucket sorting, which requiresO(n) expected time in this case despite the probability dependency between the edge weights.     

Surface reconstruction by layer peeling

Given an input point cloud P in ?³, this paper proposes a novel algorithm to identify surface neighbors of each point p∈P respecting the underlying surface S and then to construct a piecewise linear surface for P. The algorithm utilizes the simple k-nearest neighborhood in constructing local surfaces. It makes use of two concepts: a local convexity criterion to extract a set of surface neighbors for each point, and a global projection test to determine an order for the reconstruction. Our algorithm not only produces a topologically correct surface for well-sampled point sets, but also adapts well to handle under-sampled point sets. Furthermore, the computational cost of the algorithm increases almost linearly in the size of the point cloud. It, thus, scales well to deal with large input point sets.