The purpose of this dissertation is to investigate the geometry of spaces of planar quadrilaterals. The topology of moduli spaces of planar quadrilaterals (the set of all distinct planar quadrilaterals with fixed side lengths) has been well-studied [5], [8], [10]. The symplectic geometry of these spaces has been studied by Kapovich and Millson [6], but the Riemannian geometry of these spaces has not been thoroughly examined. We study paths in the moduli space and the pre-moduli space. We compare intraplanar paths between points in the moduli space to extraplanar paths between those same points. We give conditions on side lengths to guarantee that intraplanar motion is shorter between some points. Direct applications of this result could be applied to motion-planning of a robot arm. We show that horizontal lifts to the pre-moduli space of paths in the moduli space can exhibit holonomy. We determine exactly which collections of side lengths allow holonomy. / Ph. D.

Visual sensitivity to small color difference is an important factor for precision color matching. Small color differences can be measured by the line element theory in terms of color distances between a color point and neighborhoods of points in a color space. This theory gives a smooth positive definite symmetric metric tensor which describes threshold of color differences by ellipsoids in three dimensions and ellipses in two dimensions. The metric tensor is also known as the Riemannian metric tensor. In regard to the color differences, there are many color difference formulas and color spaces to predict visual difference between two colors but, it is still challenging due to the nonexistence of a perfect uniform color space. In such case, the Riemannian metric tensor can be used as a tool to study the performance of various color spaces and color difference metrics for measuring the perceptual color differences. It also computes the shortest length or the distance between any two points in a color space. The shortest length is called a geodesic. According to Schrödinger's hypothesis geodesics starting from the neutral point of a surface of constant brightness correspond to the curves of constant hue. The chroma contours are closed curves at constant intervals from the origin measured as the distance along the constant hue geodesics. This hypothesis can be utilized to test the performance of color difference formulas to predict perceptual attributes (hue and chroma) and distribution of color stimulus in any color space. In this research work, a method to formulate line element models of color difference formulas the ΔE*ab, the ΔE*uv, the OSA-UCS ΔEE and infinitesimal approximation of CIEDE2000 (ΔE00) is presented. The Jacobian method is employed to transfer their Riemannian metric tensors in other color spaces. The coefficients of such metric tensors are used to compute ellipses in two dimensions. The performance of these four color difference formulas is evaluated by comparing computed ellipses with experimentally observed ellipses in different chromaticity diagrams. A method is also developed for comparing the similarity between a pair of ellipses. The technique works by calculating the ratio of the area of intersection and the area of union of a pair of ellipses. Similarly, at a fixed value of lightness L*, hue geodesics originating from the achromatic point and their corresponding chroma contours of the above four formulas in the CIELAB color space are computed by solving the Euler-Lagrange equations in association with their Riemannian metrics. They are compared with with the Munsell chromas and hue circles at the Munsell values 3, 5 and 7. The result shows that neither formulas are fully perfect for matching visual color difference data sets. However, Riemannized ΔE00 and the ΔEE formulas measure the visual color differences better than the ΔE*ab and the ΔE*uv formulas at local level. It is interesting to note that the latest color difference formulas like the OSA-UCS ΔEE and the Riemannized ΔE00 do not show better performance to predict hue geodesics and chroma contours than the conventional CIELAB and CIELUV color difference formulas and none of these formulas fit the Munsell data accurately

Given a compact smooth manifold Mn without boundary and n ≥ 3, the Lp-norm of the curvature tensor, defines a Riemannian functional on the space of Riemannian metrics with unit volume M1. Consider C2,α-topology on M1 Rp remains invariant under the action of the group of diffeomorphisms D of M. So, Rp is defined on M1/ D. Our first result is that Rp restricted to the space M1/D has strict local minima at Riemannian metrics with constant sectional curvature for certain values of p. The product of spherical space forms and the product of compact hyperbolic manifolds are also critical point for Rp if they are product of same dimensional manifolds. We prove that these spaces are strict local minima for Rp restricted to M1/D. Compact locally symmetric isotropy irreducible metrics are critical points for Rp. We give a criteria for the local minima of Rp restricted to the conformal class of metrics of a given irreducible symmetric metric. We also prove that the metrics with constant bisectional curvature are strict local minima for Rp restricted to the space of Kahlar metrics with unite volume quotient by D. Next we consider the Riemannian functional given by In [GV], M. J. Gursky and J. A. Viaclovsky studied the local properties of the moduli space of critical metrics for the functional Ric2.We generalize their results for any p > 0.

Afin de pouvoir apparier de manière précise les couleurs il est essentiel de prendre en compte la sensibilité visuelle à percevoir de petites différences de couleur. Les petites différences de couleur peuvent être mesurées par des ellipses qui décrivent les différences justes observables (just noticeable difference - JND). Ces ellipses décrivent la faculté du Système Visuel Humain à discriminer des couleurs très peu différentes. D'un point de vue mathématique, ces ellipses peuvent être modélisées par une fonction différentielle positive de forme quadratique, caractéristique de ce que l'on appelle communément une métrique Riemannienne. La métrique Riemannienne peut être considérée comme un outil utile pour évaluer l'adéquation, la robustesse et la précision, d'un espace couleur ou d'une métrique couleur, à décrire, à mesurer, correctement les différences de couleur telles qu'elles sont perçues par le Système Visuel Humain. L'un des particularités de cette métrique est qu'elle modélise la plus petite distance qui sépare deux couleurs dans un espace couleur par une ligne géodésique. Selon l'hypothèse de Schrödinger les lignes géodésiques qui partent d'un point neutre d'une surface de luminosité constante décrivent des courbes de teinte constante. Les contours de chrominance (chroma) forment alors des courbes fermées à intervalles constants à partir de ce point neutre situées à une distance constante des lignes géodésiques associées à ces teintes constances. Cette hypothèse peut être utilisée pour tester la robustesse, la précision, des formules mathématiques utilisées pour mesurer des différences couleur (color difference formulas) et pour prédire quelle valeurs peuvent prendre tel ou tel attribut perceptuel, ex. la teinte et la saturation (hue and chroma), ou telle distribution de stimulus couleur, dans n'importe quel espace couleur. Dans cette thèse, nous présentons une méthode qui permet de modéliser les éléments de ligne (lignes géodésiques), correspondants aux formules mathématiques Delta E * ab, Delta E * uv, OSA-UCS Delta EE utilisées pour mesurer des différences couleur, ainsi que les éléments de ligne correspondants à l'approximation infinitésimales du CIEDE2000. La pertinence de ces quatre formules mathématiques a été évaluée par comparaison, dans différents plans de représentation chromatique, des ellipses prédites et des ellipses expérimentalement obtenues par observation visuelle. Pour chacune de ces formules mathématiques, nous avons également testé l'hypothèse de Schrödinger, en calculant à partir de la métrique Riemannienne, les lignes géodésiques de teinte et les contours de chroma associés, puis en comparant les courbes calculées dans l'espace couleur CIELAB avec celles obtenues dans le système Munsell. Les résultats que nous avons obtenus démontrent qu'aucune de ces formules mathématiques ne prédit précisément les différences de couleur telles qu'elles sont perçues par le Système Visuel Humain. Ils démontrent également que les deux dernières formules en date, OSA-UCS Delta EE et l'approximation infinitésimale du CIEDE2000, ne sont pas plus précises que les formules conventionnelles calculées à partir des espaces couleur CIELAB et CIELUV, quand on se réfère au système Munsell (Munsell color order system) / Visual sensitivity to small color difference is an important factor for precision color matching. Small color differences can be measured by the line element theory in terms of color distances between a color point and neighborhoods of points in a color space. This theory gives a smooth positive definite symmetric metric tensor which describes threshold of color differences by ellipsoids in three dimensions and ellipses in two dimensions. The metric tensor is also known as the Riemannian metric tensor. In regard to the color differences, there are many color difference formulas and color spaces to predict visual difference between two colors but, it is still challenging due to the nonexistence of a perfect uniform color space. In such case, the Riemannian metric tensor can be used as a tool to study the performance of various color spaces and color difference metrics for measuring the perceptual color differences. It also computes the shortest length or the distance between any two points in a color space. The shortest length is called a geodesic. According to Schrödinger's hypothesis geodesics starting from the neutral point of a surface of constant brightness correspond to the curves of constant hue. The chroma contours are closed curves at constant intervals from the origin measured as the distance along the constant hue geodesics. This hypothesis can be utilized to test the performance of color difference formulas to predict perceptual attributes (hue and chroma) and distribution of color stimulus in any color space. In this research work, a method to formulate line element models of color difference formulas the ΔE*ab, the ΔE*uv, the OSA-UCS ΔEE and infinitesimal approximation of CIEDE2000 (ΔE00) is presented. The Jacobian method is employed to transfer their Riemannian metric tensors in other color spaces. The coefficients of such metric tensors are used to compute ellipses in two dimensions. The performance of these four color difference formulas is evaluated by comparing computed ellipses with experimentally observed ellipses in different chromaticity diagrams. A method is also developed for comparing the similarity between a pair of ellipses. The technique works by calculating the ratio of the area of intersection and the area of union of a pair of ellipses. Similarly, at a fixed value of lightness L*, hue geodesics originating from the achromatic point and their corresponding chroma contours of the above four formulas in the CIELAB color space are computed by solving the Euler-Lagrange equations in association with their Riemannian metrics. They are compared with with the Munsell chromas and hue circles at the Munsell values 3, 5 and 7. The result shows that neither formulas are fully perfect for matching visual color difference data sets. However, Riemannized ΔE00 and the ΔEE formulas measure the visual color differences better than the ΔE*ab and the ΔE*uv formulas at local level. It is interesting to note that the latest color difference formulas like the OSA-UCS ΔEE and the Riemannized ΔE00 do not show better performance to predict hue geodesics and chroma contours than the conventional CIELAB and CIELUV color difference formulas and none of these formulas fit the Munsell data accurately

In this thesis we prove several results on the structure of solutions to optimal transportation problems. The second chapter represents joint work with Robert McCann and Micah Warren; the main result is that, under a non-degeneracy condition on the cost function, the optimal is concentrated on a $n$-dimensional Lipschitz submanifold of the product space. As a consequence, we provide a simple, new proof that the optimal map satisfies a Jacobian equation almost everywhere. In the third chapter, we prove an analogous result for the multi-marginal optimal transportation problem; in this context, the dimension of the support of the solution depends on the signatures of a $2^{m-1}$ vertex convex polytope of semi-Riemannian metrics on the product space, induce by the cost function. In the fourth chapter, we identify sufficient conditions under which the solution to the multi-marginal problem is concentrated on the graph of a function over one of the marginals. In the fifth chapter, we investigate the regularity of the optimal map when the dimensions of the two spaces fail to coincide. We prove that a regularity theory can be developed only for very special cost functions, in which case a quotient construction can be used to reduce the problem to an optimal transport problem between spaces of equal dimension. The final chapter applies the results of chapter 5 to the principal-agent problem in mathematical economics when the space of types and the space of available goods differ. When the dimension of the space of types exceeds the dimension of the space of goods, we show if the problem can be formulated as a maximization over a convex set, a quotient procedure can reduce the problem to one where the two dimensions coincide. Analogous conditions are investigated when the dimension of the space of goods exceeds that of the space of types.

In this thesis we prove several results on the structure of solutions to optimal transportation problems. The second chapter represents joint work with Robert McCann and Micah Warren; the main result is that, under a non-degeneracy condition on the cost function, the optimal is concentrated on a $n$-dimensional Lipschitz submanifold of the product space. As a consequence, we provide a simple, new proof that the optimal map satisfies a Jacobian equation almost everywhere. In the third chapter, we prove an analogous result for the multi-marginal optimal transportation problem; in this context, the dimension of the support of the solution depends on the signatures of a $2^{m-1}$ vertex convex polytope of semi-Riemannian metrics on the product space, induce by the cost function. In the fourth chapter, we identify sufficient conditions under which the solution to the multi-marginal problem is concentrated on the graph of a function over one of the marginals. In the fifth chapter, we investigate the regularity of the optimal map when the dimensions of the two spaces fail to coincide. We prove that a regularity theory can be developed only for very special cost functions, in which case a quotient construction can be used to reduce the problem to an optimal transport problem between spaces of equal dimension. The final chapter applies the results of chapter 5 to the principal-agent problem in mathematical economics when the space of types and the space of available goods differ. When the dimension of the space of types exceeds the dimension of the space of goods, we show if the problem can be formulated as a maximization over a convex set, a quotient procedure can reduce the problem to one where the two dimensions coincide. Analogous conditions are investigated when the dimension of the space of goods exceeds that of the space of types.

In the first chapter of this report, our aim is to introduce harmonic maps between Riemann surfaces using the Energy integral of a map. Once we have the desired prerequisites, we move on to show how to continuously deform a given map to a harmonic map (i.e., find a harmonic map in its homotopy class). We follow J¨urgen Jost’s approach using classical potential theory techniques. Subsequently, we analyze the additional conditions needed to ensure a certain uniqueness property of harmonic maps within a given homotopy class. In conclusion, we look at a couple of applications of what we have shown thus far and we find a neat proof of a slightly weaker version of Hurwitz’s Automorphism Theorem. In the second chapter, we introduce the concept of minimal surfaces. After exploring a few examples, we mathematically formulate Plateau’s problem regarding the existence of a soap film spanning each closed, simple wire frame and discuss a solution. In conclusion, a partial result (due to Rad´o) regarding the uniqueness of such a soap film is discussed.

Ce manuscrit étudie les images omnidirectionnelles catadioptriques couleur en tant que variétés Riemanniennes. Cette représentation géométrique ouvre des pistes intéressantes pour résoudre les problèmes liés aux distorsions introduites par le système catadioptrique dans le cadre de la perception couleur des systèmes autonomes. Notre travail démarre avec un état de l’art sur la vision omnidirectionnelle, les différents dispositifs et modèles de projection géométriques. Ensuite, nous présentons les notions de base de la géométrie Riemannienne et son utilisation en traitement d’images. Ceci nous amène à introduire les opérateurs différentiels sur les variétés Riemanniennes, qui nous seront utiles dans cette étude. Nous développons alors une méthode de construction d’un tenseur métrique hybride adapté aux images catadioptriques couleur. Ce tenseur a la double caractéristique, de dépendre de la position géométrique des points dans l’image, et de leurs coordonnées photométriques également. L’exploitation du tenseur métrique proposé pour différents traitements des images catadioptriques, est une partie importante dans cette thèse. En effet, on constate que la fonction Gaussienne est au cœur de plusieurs filtres et opérateurs pour diverses applications comme le débruitage, ou bien l’extraction des caractéristiques bas niveau à partir de la représentation dans l’espace-échelle Gaussien. On construit ainsi un nouveau noyau Gaussien dépendant du tenseur métrique Riemannien. Il présente l’avantage d’être applicable directement sur le plan image catadioptrique, également, variable dans l’espace et dépendant de l’information image locale. Dans la dernière partie de cette thèse, nous discutons des applications robotiques de la métrique hybride, en particulier, la détection de l’espace libre navigable pour un robot mobile, et nous développons une méthode de planification de trajectoires optimal. / This manuscript investigates omnidirectional catadioptric color images as Riemannian manifolds. This geometric representation offers insights into the resolution of problems related to the distortions introduced by the catadioptric system in the context of the color perception of autonomous systems. The report starts with an overview of the omnidirectional vision, the different used systems, and the geometric projection models. Then, we present the basic notions and tools of Riemannian geometry and its use in the image processing domain. This leads us to introduce some useful differential operators on Riemannian manifolds. We develop a method of constructing a hybrid metric tensor adapted to color catadioptric images. This tensor has the dual characteristic of depending on the geometric position of the image points and their photometric coordinates as well.In this work, we mostly deal with the exploitation of the previously constructed hybrid metric tensor in the catadioptric image processing. Indeed, it is recognized that the Gaussian function is at the core of several filters and operators for various applications, such as noise reduction, or the extraction of low-level characteristics from the Gaussian space- scale representation. We thus build a new Gaussian kernel dependent on the Riemannian metric tensor. It has the advantage of being applicable directly on the catadioptric image plane, also, variable in space and depending on the local image information. As a final part in this thesis, we discuss some possible robotic applications of the hybrid metric tensor. We propose to define the free space and distance transforms in the omni- image, then to extract geodesic medial axis. The latter is a relevant topological representation for autonomous navigation, that we use to define an optimal trajectory planning method.

Abstract in English below.