Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-05-29T13:28:57Z No. of bitstreams: 1 douglasferreirabrum.pdf: 632780 bytes, checksum: 1da883a558292ba219387c3fdf6f98af (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-05-29T19:37:42Z (GMT) No. of bitstreams: 1 douglasferreirabrum.pdf: 632780 bytes, checksum: 1da883a558292ba219387c3fdf6f98af (MD5) / Made available in DSpace on 2017-05-29T19:37:42Z (GMT). No. of bitstreams: 1 douglasferreirabrum.pdf: 632780 bytes, checksum: 1da883a558292ba219387c3fdf6f98af (MD5) Previous issue date: 2013-08-30 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Esse trabalho visa descrever o espaço de Módulos de m-uplas geodésicas complexas distintas em H2c nos casos regular, especial e degenerado. Para tal fim faremos uso da matriz de Gram e dos invariantes (d-invariantes, δ-invariantes, invariante angular e invariantes parabólicos) que descrevem unicamente a classe de congruência de PU(2, 1) de m-uplas ordenadas de geodésicas complexas distintas nos diferentes casos supracitados. / This work aims to describe the Modules space of m-tuples distinct complex geodesics in H2c in the cases regular, special and degenerate. To this end we use the Gram matrix and the invariant (d-invariant, δ-invariants, angular invariant and parabolic invariants) that define uniquely the PU(2,1)-congruence class of ordered m-uplas of distinct complex geodesics in the different cases above.

Cette thèse aborde la question du traitement correct et complet de la couleur selon les contraintes métrologiques. Le manque d’approches adaptées a justifié la reformulation principaux outils de traitement d’images que sont le gradient, la détection et la description de points d’intérêt. Les approches proposées sont génériques : indépendantes du nombre de canaux d’acquisition (de la couleur à l’hyper-spectral), de la plage spectrale considérée et prenant en compte les courbes de sensibilité spectrales du capteur ou de l’œil.Le full-vector gradient nait de cet objectif métrologique. La preuve de concept est effectuée sur des images couleurs, multi et hyper-spectrales. L’extension développée pour l’analyse de la déficience visuelle ouvre également de nombreuses s perspectives intéressantes pour l’analyse du système visuel humain. Ce gradient est au cœur de la proposition d’un détecteur de points d’intérêt, lui aussi générique. Nous montrons la nécessité d’un choix mathématiquement valide de la distance entre attributs et l’importance de la cohérence de la paire attribut/distance. Une paire attribut/distance complète l’ensemble.Pour chaque développement, nous proposons des protocoles objectifs de validation liés à des générateurs d’images de synthèse explorant toute la complexité spatio-chromatique possible. Notre hypothèse est que la difficulté d’extraction du gradient/des points d’intérêts… est liée à la complexité de discrimination des distributions couleur dans la zone de traitement. Une confrontation aux approches courantes du domaine a été également mise en œuvre. / The PhD thesis objective is to study a colour’s correct and complete processing, respecting metrological constraint. The lack of compatible approaches justified that we reformulate the main image processing tools that are gradient, key point detector and descriptor. The proposed approaches are generic: channel count independent and taking the sensor’s or eye’s sensitivity curves into account.The full-vector gradient is born from this metrological objective. Proof of concept was realised on colour, multi and hyper-spectral images. The extension developed for human vision deficiency opens interesting perspectives to study of the human vision system. This gradient is the centre of the key point detector proposition, also generic.We also showed how necessary was a mathematically valid choice of distance between features. We revealed the importance of the pair feature/distance and completed the work with a pair: RC2O/Kulback-Leibler divergence based on colour differences.For each development, we propose unbiased validation protocols linked to synthetic images generators exploring the most spatial-chromatic complexity possible. Our hypothesis being that the extraction difficulty comes from the discrimination complexity between colour distributions in the processing area. We also compared our proposition to state of the art approaches in recurring datasets/protocols.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / Neste trabalho abordaremos alguns tÃpicos matriciais e determinantes e sua aplicaÃÃo no Ensino MÃdio. Em especial a Matriz de Gram em uma transformaÃÃo linear que pode ser apli-cada, por exemplo, para calcular a Ãrea de um triÃngulo em funÃÃo dos seus lados e tambÃm o Gramiano (determinante da Matriz de Gram) que permite calcular o volume de um paralele-pÃpedo. Ambos podem ser aplicados no ensino mÃdio. Nesse trabalho tembÃm fazemos uma generalizaÃÃo do produto vetorial e algumas de suas propriedades envolvendo determinantes. Por fim mostramos a Identidade de Lagrange. / In this paper we discuss some topics and determinants matrix and its application in high school. In particular, the Gram matrix in a linear transformation which can be applied, for example, to calculate the area of a triangle in terms of their sides and also Gramiano (Gram matrix determinant) for calculating the volume of a parallelepiped. Both can be applied in high school. In this work we tembÃm a generalization of the vector product and some of its properties involving determinants. Finally we show the identity of Lagrange.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-02-25T10:58:39Z No. of bitstreams: 1 rafaeldasilvalima.pdf: 522996 bytes, checksum: 5a2e8f3b92223160a83315d7eb1cac50 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-03-03T13:26:29Z (GMT) No. of bitstreams: 1 rafaeldasilvalima.pdf: 522996 bytes, checksum: 5a2e8f3b92223160a83315d7eb1cac50 (MD5) / Made available in DSpace on 2016-03-03T13:26:29Z (GMT). No. of bitstreams: 1 rafaeldasilvalima.pdf: 522996 bytes, checksum: 5a2e8f3b92223160a83315d7eb1cac50 (MD5) Previous issue date: 2014-04-11 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O objetivo desse trabalho, é a construção do espaço de módulos para o conjunto de quá- druplas ordenadas de pontos na fronteira do espaço hiperbólico complexo. Para isso, utilizaremos o conceito de matriz de Gram como critério de congruência, e a parametrização do espaço de con gurações será feito pelo invariante angular de Cartan e a razão-cruzada. Exempli caremos algumas situações geométricas. / The aim of this work is the construction of a moduli space for the con guration space ordered quadruples of points on the boundary of the complex hyperbolic space. For this use the concept of Gram matrix as a criterion of congruence, and parametrization the con guration space will be done by the Cartan invariant and cross-ratio. Will be exempli ed some geometric situations.

<p dir="ltr">The generalization error of a classifier is related to the complexity of the set of functions among which the classifier is chosen. We study a family of low-complexity classifiers consisting of thresholding a random one-dimensional feature. The feature is obtained by projecting the data on a random line after embedding it into a higher-dimensional space parametrized by monomials of order up to k. More specifically, the extended data is projected n-times and the best classifier among those n, based on its performance on training data, is chosen. </p><p dir="ltr">We show that this type of classifier is extremely flexible, as it is likely to approximate, to an arbitrary precision, any continuous function on a compact set as well as any Boolean function on a compact set that splits the support into measurable subsets. In particular, given full knowledge of the class conditional densities, the error of these low-complexity classifiers would converge to the optimal (Bayes) error as k and n go to infinity. On the other hand, if only a training dataset is given, we show that the classifiers will perfectly classify all the training points as k and n go to infinity. </p><p dir="ltr">We also bound the generalization error of our random classifiers. In general, our bounds are better than those for any classifier with VC dimension greater than O(ln(n)). In particular, our bounds imply that, unless the number of projections n is extremely large, there is a significant advantageous gap between the generalization error of the random projection approach and that of a linear classifier in the extended space. Asymptotically, as the number of samples approaches infinity, the gap persists for any such n. Thus, there is a potentially large gain in generalization properties by selecting parameters at random, rather than optimization. </p><p dir="ltr">Given a classification problem and a family of classifiers, the Rashomon ratio measures the proportion of classifiers that yield less than a given loss. Previous work has explored the advantage of a large Rashomon ratio in the case of a finite family of classifiers. Here we consider the more general case of an infinite family. We show that a large Rashomon ratio guarantees that choosing the classifier with the best empirical accuracy among a random subset of the family, which is likely to improve generalizability, will not increase the empirical loss too much. </p><p dir="ltr">We quantify the Rashomon ratio in two examples involving infinite classifier families in order to illustrate situations in which it is large. In the first example, we estimate the Rashomon ratio of the classification of normally distributed classes using an affine classifier. In the second, we obtain a lower bound for the Rashomon ratio of a classification problem with a modified Gram matrix when the classifier family consists of two-layer ReLU neural networks. In general, we show that the Rashomon ratio can be estimated using a training dataset along with random samples from the classifier family and we provide guarantees that such an estimation is close to the true value of the Rashomon ratio.</p>