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Publication# Constructing reparameterization invariant metrics on spaces of plane curves

Résumé

Metrics on shape spaces are used to describe deformations that take one shape to another, and to define a distance between shapes. We study a family of metrics on the space of curves, which includes several recently proposed metrics, for which the metrics are characterised by mappings into vector spaces where geodesics can be easily computed. This family consists of Sobolev-type Riemannian metrics of order one on the space Imm(S-1, R-2) of parameterized plane curves and the quotient space Imm(S-1,R-2)/Diff (S-1) of unparameterized curves. For the space of open parameterized curves we find an explicit formula for the geodesic distance and show that the sectional curvatures vanish on the space of parameterized open curves and are non-negative on the space of unparameterized open curves. For one particular metric we provide a numerical algorithm that computes geodesics between unparameterized, closed curves, making use of a constrained formulation that is implemented numerically using the RATTLE algorithm. We illustrate the algorithm with some numerical tests between shapes. (C) 2014 Elsevier B.V. All rights reserved.

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Géodésique

En géométrie, une géodésique est la généralisation d'une ligne droite du plan ou de l'espace euclidien, au cadre des surfaces, ou plus généralement des variétés ou des espaces métriques. Elles sont ét

Espace (notion)

L'espace se présente dans l'expérience quotidienne comme une notion de géométrie et de physique qui désigne une étendue, abstraite ou non, ou encore la perception de cette étendue. Conceptuellement,

Forme (géométrie)

En géométrie classique, la forme permet d’identifier ou de distinguer des figures selon qu’elles peuvent ou non être obtenues les unes à partir des autres par des transformations géométriques qui prés

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Since the birth of Information Theory, researchers have defined and exploited various information measures, as well as endowed them with operational meanings. Some were born as a "solution to a problem", like Shannon's Entropy and Mutual Information. Others were the fruit of generalisation and the mathematical genius of bright minds like Rényi, Csizsár and Sibson. These powerful objects allow us to manipulate probabilities intuitively and seem always to be somehow connected to concrete settings in communication, coding or estimation theory. A common theme is: take a problem in one of these areas, try to control (upper or lower-bound) the expected value of some function of interest (often, probabilities of error) and, with enough work, an information measure appears as a fundamental limit of the problem. The most striking example of this is in Shannon's seminal paper in 1948: his purpose was to characterise the smallest possible expected length of a uniquely decodable encoding that compresses the realisations of a random variable. As he brilliantly proved, the smallest expected length one can hope for is the Entropy of the random variable. In establishing this connection, another quantity needed to be implicitly controlled: the Kraft's sum of the code. Seemingly unrelated before, these three objects joined forces in harmony to provide a beautiful and fundamental result. But why are they related? The answer seems to be: duality. Duality is an abstract notion commonly used in linear algebra and functional analysis. It has been expanded and generalised over the years. Several incarnations have been discovered throughout mathematics. One particular instance of this involves vector spaces: given two vector spaces and a "duality pairing" one can jump from one space to the other (its dual) through Legendre-Fenchel-like transforms. In the most common settings in Information Theory, the two spaces and the pairing are, respectively: 1) the space of (probability)measures defined on X; 2) the space of bounded functions defined on X; 3) the Lebesgue integral of the function (the expected value of the function if the measure is a probability measure). Once these are set, Legendre-Fenchel-like transforms allow us to connect a) a functional acting on the space described in 1), b) a functional acting on the space described in 2) and the anchor point is c) the (expected) value described in 3).These three pieces (a), b) and c)) represent the actors of many of the results provided in Information Theory. Once they are found, one usually bounds the functional described in b) and obtains a bound connecting the expected value and the functional of measures (e.g., an information measure). Going back to Shannon's result, fixed a random variable (and thus, a probability measure) and selected the function to be the length of a code: the functional a) is the Shannon Entropy of the source; the functional b) is the Kraft sum of the code; the pairing c) is the expected length of the code. We explore this connection and this pattern throughout the thesis. We will see how it can be found in notable results like Coding Theorems for one-to-one codes, Campbell's Coding Theorem, Arikan's Guessing Theorem, Fano-like and Transportation-Cost Inequalities and so on. Moreover, unearthing the pattern allows us to generalise it to other information measures and apply the technique in a variety of fields, including Learning Theory, Estimation Theory and Hypothesis Testing.

The authors propose a numerical method for the uniformization of Riemann surfaces and algebraic curves in genus two with highly accurate results. Let $G$ be a Fuchsian group acting on the unit disk $Bbb D$, and let $S = Bbb D / G$. It is well known that $S$ is also in natural way an algebraic curve. The authors describe a practical way to compute, in genus 2, the uniformizing function from the unit disk to an algebraic curve. The basic idea underlying the method is to reduce the problem to the known case of genus 1. Moreover, they produce an algorithm to compute an approximation of uniformizing functions (the algorithm code and results of computations can be found at the second named author's homepage.

The distance from self-intersection of a (smooth and either closed or infinite) curve q in three dimensions can be characterised via the global radius of curvature at q(s), which is defined as the smallest possible radius amongst all circles passing through the given point and any two other points on the curve. The minimum value of the global radius of curvature along the curve gives a convenient measure of curve thickness or normal injectivity radius. Given the utility of the construction inherent to global curvature, it is natural to consider variants defined in related ways. The first part of the thesis considers all possible circular and spherical distance functions and the associated, single argument, global radius of curvature functions that are constructed by minimisation over all but one argument. It is shown that among all possible global radius of curvature functions there are only five independent ones. And amongst these five there are two particularly useful ones for characterising thickness of a curve. We investigate the geometry of how these two functions, ρpt and ρtp, can be achieved. Properties and interrelations of the divers global radius of curvature functions are illustrated with the simple examples of ellipses and helices. It is known that any Lipschitz continuous curve with positive thickness actually has C1,1-regularity. Accordingly, C1,1 is the natural space in which to carry out computations involving self-avoiding curves. The second part of the thesis develops the mathematical theory of biarcs, which are a geometrically elegant way of discretizing C1,1 space curves. A biarc is a pair of circular arcs joined in a C1 fashion according to certain matching rules. We establish a self-contained theory of the geometry of biarc interpolation of point-tangent data sampled from an underlying base curve, and demonstrate that such biarc curves have attractive convergence properties in both a pointwise and function-space sense, e.g. the two arcs of the biarc interpolating a coalescent point-tangent data pair on a C2-curve approach the osculating circle of the curve at the limit of the data points, and for a C1,1-base curve and a sequence of (possibly non-uniform) meshes, the interpolating biarc curves approach the base curve in the C1-norm. For smoother base curves, stronger convergence can be obtained, e.g. interpolating biarc curves approach a C2 base curve in the C1,1-norm. The third part of the thesis concerns the practical utility of biarcs in computation. It is shown that both the global radius of curvature function ρpt and thickness can be evaluated efficiently (and to an arbitrarily small, prescribed precision) on biarc curves. Moreover, both the notion of a contact set, i.e. the set of points realising thickness, and an approximate contact set can be defined rigorously. The theory is then illustrated with an application to the computation of ideal shapes of knots. Informally ideal knot shapes can be described as the configuration allowing a given knot to be tied with the shortest possible piece of rope of prescribed thickness. The biarc discretization is combined with a simulated annealing code to obtain approximate ideal shapes. These shapes provide rigorous upper bounds for rope length of ideal knots. The approximate contact set and the function ρpt evaluated on the computed shapes allow us to assess closeness of the computations to ideality. The high accuracy of the computations reveal various, previously unrecognized, features of ideal knot shapes.