k-dimensional Size Functions for shape description and comparison

This paper advises the use of k-dimensional size functions for comparison and retrieval in the context of multidimensional shapes, where by shape we mean something in two or higher dimensions having a visual appearance. The attractive feature of k-dimensional size functions is that they allow to readily establish a similarity measure between shapes of arbitrary dimension, taking into account different properties expressed by a multivalued real function defined on the shape. This task is achieved through a particular projection of k-dimensional size functions into the 1-dimensional case. Therefore, previous results on the stability for matching purposes become applicable to a wider range of data. We outline the potential of our approach in a series of experiments

Formenvergleich in höheren Dimensionen

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

SPIDA: Abstracting and generalizing layout design cases

Abstraction and generalization of layout design cases generate new knowledge that is more widely applicable to use than specific design cases. The abstraction and generalization of design cases into hierarchical levels of abstractions provide the designer with the flexibility to apply any level of abstract and generalized knowledge for a new layout design problem. Existing case-based layout learning (CBLL) systems abstract and generalize cases into single levels of abstractions, but not into a hierarchy. In this paper, we propose a new approach, termed customized viewpoint - spatial (CV-S), which supports the generalization and abstraction of spatial layouts into hierarchies along with a supporting system, SPIDA (SPatial Intelligent Design Assistant)

Wideband P-Shaped Dielectric Resonator Antenna

A novel P-shaped dielectric resonator antenna (DRA) is presented and investigated for wideband wireless application. By using P-shaped resonator, a wideband impedance bandwidth of 80% from 3.5 to 8.2 GHz is achieved. The antenna covers all of wireless systems like C-band, 5.2, 5.5 & 5.8 GHz-WLAN & WiMax. The proposed antenna has a low profile and the thickness of the resonator is only 5.12 mm, which is 0.06-0.14 free space wavelength. A parametric study is presented. The proposed DRA is built and the characteristics of the antenna are measured. Very good agreement between numerical and measured results is obtained

Kaluza-Klein States versus Winding States: Can Both Be Above the String Scale?

When closed strings propagate in extra compactified dimensions, a rich spectrum of Kaluza-Klein states and winding states emerges. Since the masses of Kaluza-Klein states and winding states play a reciprocal role, it is often believed that either the lightest Kaluza-Klein states or the lightest winding states must be at or below the string scale. In this paper, we demonstrate that this conclusion is no longer true for compactifications with non-trivial shape moduli. Specifically, we demonstrate that toroidal compactifications exist for which all Kaluza-Klein states as well as all winding states are heavier than the string scale. This observation could have important phenomenological implications for theories with reduced string scales, suggesting that it is possible to cross the string scale without detecting any states associated with spacetime compactification.Comment: 8 pages, LaTeX, no figure

A precise determination of alpha_s from LEP thrust data using effective field theory

Starting from a factorization theorem in Soft-Collinear Effective Theory, the thrust distribution in e+e- collisions is calculated including resummation of the next-to-next-to-next-to leading logarithms. This is a significant improvement over previous calculations which were only valid to next-to-leading logarithmic order. The fixed-order expansion of the resummed result approaches the exact fixed-order distribution towards the kinematic endpoint. This close agreement provides a verification of both the effective field theory expression and recently completed next-to-next-to-leading fixed order event shapes. The resummed distribution is then matched to fixed order, resulting in a distribution valid over a large range of thrust. A fit to ALEPH and OPAL data from LEP 1 and LEP 2 produces alpha_s(m_Z)= 0.1172 +/- 0.0010 +/- 0.0008 +/-0.0012 +/- 0.0012, where the uncertainties are respectively statistical, systematic, hadronic, and perturbative. This is one of the world's most precise extractions of alpha_s to date.Comment: 37 pages, 12 figures; v2: hadronization discussion and appendices expande