114,042 research outputs found

    Formenvergleich in höheren Dimensionen

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    Cover and Contents 1 Introduction 1.1 Overview 1.2 Credits 2 Preliminaries 2.1 Representation of Shapes 2.2 Distance Measures 2.3 Miscellaneous 3 Hausdorff Distance Under Translations 3.1 Overview 3.2 Basic Properties of \delta;->H 3.3 Matching Points to Sites 3.4 Matching Two Sets of Sites 3.5 Approximate Algorithms 4 Matching Special Shape Classes Under Translations 4.1 Matching Terrains 4.2 Matching Convex Polyhedra 5 Matching Curves with respect to the Fréchet Distance 5.1 Basic Properties of the Fréchet Distance 5.2 Polygonal Curves Under Translations 5.3 Polygonal Curves Under Affine Transformations 5.4 Variants 6 Matching a Polygonal Curve in a Graph of Curves 6.1 Problem Statement 6.2 Algorithm 6.3 Variants Bibliography Index A Zusammenfassung B LebenslaufThe comparison of geometric shapes is a task which naturally arises in many applications, such as in computer vision, computer aided design, robotics, medical imaging, etc. Usually geometric shapes are represented by a number of simple objects (sites) that either describe the boundary of the shape, or the whole shape itself. Sites are often chosen to be linear objects, such as line segments, triangles, or simplices in general, since linear objects are easier to handle in algorithms. But sometimes also patches of algebraic curves or surfaces, such as circular arcs or portions of spheres or cylinders are of interest. In order to compare two shapes we need to have a notion of similarity or dissimilarity, which arises from the desired application. There is a large variety of different similarity measures. Popular similarity notions are, for example, the Hausdorff distance, the area of symmetric difference, or especially for curves the turn-angle distance, or the Fréchet distance. The application usually supplies a distance measure, and furthermore a set of allowed transformations, and the task is to find a transformation that, when applied to the first object, minimizes the distance to the second one. Typical transformation classes are translations, rotations, and rigid motions (which are combinations of translations and rotations). The contribution of this thesis consists of several algorithms for matching simplicial shapes in dimensions d >= 2. The shapes are either represented as sets of simplicial objects or as polygonal curves with a given parametrization. The considered distance measures are mainly the Hausdorff distance and the Fréchet distance. In the literature most matching algorithms either attack two-dimensional problems, or consider finite point sets in higher dimensions. In the first half of this thesis we present results for the Hausdorff distance in d >= 2 dimensions under translations, for a rather general notion of simplicial shapes, as well as for some special shape classes which allow to speed up the computations. In the second half of this thesis we investigate the Fréchet distance for polygonal curves. The Fréchet distance is a natural distance measure for curves, but has not been investigated much in the literature. We present the first algorithms to optimize the Fréchet distance under various transformation classes for polygonal curves in arbitrary dimensions. In the last chapter we consider a partial matching variant in which a geometric graph and another curve are given, and we show how to find a polygonal path in the graph which minimizes the Fréchet distance to the curve.Das Vergleichen zweier geometrischer Formen ist eine Aufgabe, die aus vielerlei Anwendungen natürlich hervorgeht. Einige Anwendungen sind Computer Vision, Computer Graphik, Computer Aided Design, Robotics, medizinische Bilderverarbeitung, etc. Normalerweise werden geometrische Formen aus einfacheren Objekten zusammengesetzt, die entweder den Rand der Form oder die ganze Form ansich beschreiben. Oft verwendet werden lineare Objekte wie Strecken, Dreicke, oder Simplizes in höheren Dimensionen. Um zwei Formen zu vergleichen braucht man zunächst einen Ähnlichkeits- oder Abstandsbegriff zwischen zwei Formen, der in der Regel aus der jeweiligen Anwendung hervorgeht. Naturgemäß gibt es eine große Vielfalt solcher Abstandsmaße; eines der natürlichsten ist der Hausdorff-Abstand. Weiterhin gibt die Anwendung in der Regel eine Menge von Transformationen vor, und möchte eine Transformation finden, die, angewandt auf die erste Form, den Abstand zur zweiten Form minimiert. Diese Aufgabe wird als Matching bezeichnet. Oft verwendete Transformationsklassen sind zum Beispiel Translationen, Rotationen und starre Bewegungen (Kombinationen von Translationen und Rotationen). Diese Arbeit beschäftigt sich mit dem Matching von geometrischen Formen in Dimensionen d >= 2, die aus stückweise linearen Objekten bestehen. Die Formen sind entweder als Mengen solcher Objekte, oder als Polygonzüge, die als parametrisierte Kurven aufgefaßt werden, beschrieben. Als Abstandsmaße werden hauptsächlich der Hausdorff-Abstand und der Fréchet-Abstand betrachtet. Bisherige Ergebnisse für das Matching von Formen behandeln in der Regel entweder zweidimensionale Formen, oder Punktmengen in höheren Dimensionen. Die erste Hälfte dieser Dissertation präsentiert Ergebnisse für den Hausdorff- Abstand in d >= 2 Dimensionen unter Translationen für einen allgemein gehaltenen Formenbegriff, sowie für einige spezielle Klassen geometrischer Formen, die eine schnellere Berechnung erlauben. Die zweite Hälfte der Dissertation beschäftigt sich mit dem Matching von parametrisierten Kurven bezüglich des Fréchet-Abstandes. Obwohl der Fréchet-Abstand ein natürliches Abstandsmaß für Kurven darstellt, gibt es bisher diesbezüglich wenig Ergebnisse in der Literatur. Für parametrisierte Kurven in d >= 2 Dimensionen wird in dieser Dissertation ein Matching-Algorithmus vorgestellt, der unter Translationen und relativ allgemein gehaltenen Teilmengen der affinen Abbildungen den Fréchet-Abstand minimiert. Als letztes Ergebnis wird eine weitere Matching-Variante bezüglich des Fréchet-Abstandes vorgestellt, in der eine Teilkurve in in einem eingebetteten planaren Graphens gefunden werden soll, die den Fréchet-Abstand zu einer gegebenen Kurve minimiert

    Squarepants in a Tree: Sum of Subtree Clustering and Hyperbolic Pants Decomposition

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    We provide efficient constant factor approximation algorithms for the problems of finding a hierarchical clustering of a point set in any metric space, minimizing the sum of minimimum spanning tree lengths within each cluster, and in the hyperbolic or Euclidean planes, minimizing the sum of cluster perimeters. Our algorithms for the hyperbolic and Euclidean planes can also be used to provide a pants decomposition, that is, a set of disjoint simple closed curves partitioning the plane minus the input points into subsets with exactly three boundary components, with approximately minimum total length. In the Euclidean case, these curves are squares; in the hyperbolic case, they combine our Euclidean square pants decomposition with our tree clustering method for general metric spaces.Comment: 22 pages, 14 figures. This version replaces the proof of what is now Lemma 5.2, as the previous proof was erroneou

    A Bayesian Approach to Manifold Topology Reconstruction

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    In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifold topology. Given a noisy, probably undersampled point cloud from a one- or two-manifold, the algorithm reconstructs an approximated most likely mesh in a Bayesian sense from which the sample might have been taken. We incorporate statistical priors on the object geometry to improve the reconstruction quality if additional knowledge about the class of original shapes is available. The priors can be formulated analytically or learned from example geometry with known manifold tessellation. The statistical objective function is approximated by a linear programming / integer programming problem, for which a globally optimal solution is found. We apply the algorithm to a set of 2D and 3D reconstruction examples, demon-strating that a statistics-based manifold reconstruction is feasible, and still yields plausible results in situations where sampling conditions are violated

    Computing the Similarity Between Moving Curves

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    In this paper we study similarity measures for moving curves which can, for example, model changing coastlines or retreating glacier termini. Points on a moving curve have two parameters, namely the position along the curve as well as time. We therefore focus on similarity measures for surfaces, specifically the Fr\'echet distance between surfaces. While the Fr\'echet distance between surfaces is not even known to be computable, we show for variants arising in the context of moving curves that they are polynomial-time solvable or NP-complete depending on the restrictions imposed on how the moving curves are matched. We achieve the polynomial-time solutions by a novel approach for computing a surface in the so-called free-space diagram based on max-flow min-cut duality

    Approximating Dynamic Time Warping and Edit Distance for a Pair of Point Sequences

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    We give the first subquadratic-time approximation schemes for dynamic time warping (DTW) and edit distance (ED) of several natural families of point sequences in Rd\mathbb{R}^d, for any fixed d1d \ge 1. In particular, our algorithms compute (1+ε)(1+\varepsilon)-approximations of DTW and ED in time near-linear for point sequences drawn from k-packed or k-bounded curves, and subquadratic for backbone sequences. Roughly speaking, a curve is κ\kappa-packed if the length of its intersection with any ball of radius rr is at most κr\kappa \cdot r, and a curve is κ\kappa-bounded if the sub-curve between two curve points does not go too far from the two points compared to the distance between the two points. In backbone sequences, consecutive points are spaced at approximately equal distances apart, and no two points lie very close together. Recent results suggest that a subquadratic algorithm for DTW or ED is unlikely for an arbitrary pair of point sequences even for d=1d=1. Our algorithms work by constructing a small set of rectangular regions that cover the entries of the dynamic programming table commonly used for these distance measures. The weights of entries inside each rectangle are roughly the same, so we are able to use efficient procedures to approximately compute the cheapest paths through these rectangles
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