Une longueur de pas optimale au sens de l'erreur de reprojection algébrique pour l'ajustement de faisceaux

National audiencel'ajustement de faisceaux repose sur des techniques de minimisation non linéaires au sens des moindres carrés, comme Levenberg-Marquardt ou Gauss-Newton. Ils fournissent itérativement des déplacements relatif dans l'espace des paramètres à optimiser. Les techniques de recherche de la longueur de pas (Line Search) visent à déterminer un pas de déplacement efficace pour chaque déplacement. Nous proposons dans cet article une nouvelle technique ad hoc de Line Search pour l'ajustement de faisceaux. L'idée principale est de déterminer la longueur idéale du pas de déplacement par une approximation de l'erreur de reprojection, en substituant la distance euclidienne par la distance algébrique. Notre méthode est comparée avec plusieurs algorithmes de minimisation nonlinéaires dans différentes configurations, sur des données synthétiques et réelles. Elle améliore la convergence de la minimisation de manière efficace et rapide à chaque itération et réduit le temps de minimisation de 10% en moyenne

Implementing a multi-model estimation method

This work is realized within the scope of a general attempt to understand parametric adaptation, regarding visual perception. The key idea is to analyze how we may use multi-model parametric estimation as a 1st step towards categorization. More generally, the goal is to formalize how the notion of ``objects'' or ``events'' in an application may be reduced to a choice in a hierarchy of parametric models used to estimate the underlying data categorization. These mechanisms are to be linked with what occurs in the cerebral cortex where object recognition corresponds to a parametric neuronal estimation (see for instanced Page 2000 for a discussion and Freedman et al 2001 for an example regarding the primate visual cortex). We thus hope to bring here an algorithmic element in relation with the ``grand-ma'' neuron modelization. We thus revisit the problem of parameter estimation in computer vision, presented here as a simple optimization problem, considering (i) non-linear implicit measurement equations and parameter constraints, plus (ii) robust estimation in the presence of outliers and (iii) multi-model comparisons. Here, (1) a projection algorithm based on generalizations of square-root decompositions allows an efficient and numerically stable local resolution of a set of non-linear equations. On the other hand, (2) a robust estimation module of a hierarchy of non-linear models has been designed and validated. A step ahead, the software architecture of the estimation module is discussed with the goal of being integrated in reactive software environments or within applications with time constraints

Sister Mary Regina Donovan, S.S.J. (Nora Donovan)

Thin silicon dioxide layers (40-100 A) can be successfully produced by anodization of silicon in pure water. The resulting layers are very homogeneous and pinhole free. The monitoring of the SiO2 thickness is accurately achieved by simple coulometry. The electrical properties of the oxide layers and the associated Si/SiO2 interface have been investigated by forming metal-oxide-semiconductor (MOS) capacitors using the anodically grown oxide as the dielectric and aluminium or chromium as the metal. This investigation shows a low charge density at the Si/SiO2 interface (≤ 10 11 charges. cm-2) and an interface states density comparable to that obtained with thermally grown SiO2 (1011 cm -2eV-1). The dielectric breakdown occurs at high fields (11 to 14 MW . cm-1). These results show that there is no pollution during the electrolysis. Furthermore, the metal to oxide barrier heights remained high (2.5 to 2.8 eV) even for thin (44 A) SiO2 layers

Hierarchical Visual Perception without Calibration

We have analyzed the equations and the formalism which allow to achieve dynamic visual perception of geometric and kinematic 3D information, for a monocular visual system without calibration. Considering the emergence of active visual systems for which we {\bf can not} consider that the calibration parameters are either known or fixed, we develop an alternative strategy based on the two complementary facts that: (i) several perceptual tasks can be performed without knowing the calibration parameters, while, for other perceptual tasks: (ii) certain class of special displacements induce enough equations to evaluate the calibration parameters, so that we can recover the Euclidean structure of the scene when needed. A synthesis of what can be recovered in terms of scene geometry and kinematics is proposed. We give, for the different levels of calibration, an exhaustive list of the geometric and kinematic information which can be recovered. Following a strategy based on special kind of displacements, such as fixed axis rotations or pure translations for instance, we also describe how to control the robotic system in order to generate these particular classes of displacement. The implementation of these equations is analyzed here, and some experimental results are reported

Modélisation explicite des objets et de l'environnement en combinant les approches topologique et métrique pour la localisation

Session: SLAM et localisation - http://orasis2011.inrialpes.fr/programmeNational audienceThis paper presents a new formulation of place recognition in unknown environments. Many objects in indoor environments are likely to be moved. We want to make the most of several observations of a camera in the same scene to represent the different places and objects. We propose to combine methods of metrical and topological localization to detect and model objects. We present also a probabilistic approach for place recognition that considers the objects.Nous présentons dans cet article une nouvelle formulation de la modélisation et de la reconnaissance de lieu. Les milieux intérieurs sont composés d'une quantité d'objets susceptibles d'être déplacés. Nous souhaitons exploiter les multiples passages d'une caméra dans un même environnement pour modéliser d'une part la structure de la scène et d'autre part les objets le constituant. Nous proposons une association de méthodes de SLAM métrique et topologique pour détecter et représenter les objets, ainsi qu'un formalisme probabiliste pour la reconnaissance de lieu prenant en compte l'évolution des objets

Détection et apprentissage automatique d'objets pour la modélisation de milieux intérieurs

Session "Posters" - Site conf.: http://liris.cnrs.fr/rfia2012National audienceNous présentons dans cet article une nouvelle méthode pour la modélisation des objets et de la scène dans un environnement intérieur inconnu. Les milieux intérieurs sont composés d'une quantité d'objets susceptibles d'être déplacés. Nous souhaitons exploiter les multiples passages d'une caméra dans un même lieu et tirer parti de ces déplacements pour modéliser d'une part la structure de la scène et d'autre part les objets le constituant. Nous proposons une association de méthodes de SLAM métrique et de reconnaissance de lieu pour détecter et représenter les objets de façon automatique et enrichir la connaissance de la scène. Abstract: This paper presents a new solution for modeling the scene and the objects in unknown environments. Many objects in indoor environments are likely to be moved. We want to make the most of several observations of a camera in the same scene to represent the different places and objects. We propose to combine methods of metrical localization and place recognition to detect and model objects and extend the scene model

Implementing a multi-model estimation method

This work is realized within the scope of a general attempt to understand parametric adaptation, regarding visual perception. The key idea is to analyze how we may use multi-model parametric estimation as a 1st step towards categorization. More generally, the goal is to formalize how the notion of ``objects'' or ``events'' in an application may be reduced to a choice in a hierarchy of parametric models used to estimate the underlying data categorization. These mechanisms are to be linked with what occurs in the cerebral cortex where object recognition corresponds to a parametric neuronal estimation (see for instanced Page 2000 for a discussion and Freedman et al 2001 for an example regarding the primate visual cortex). We thus hope to bring here an algorithmic element in relation with the ``grand-ma'' neuron modelization. We thus revisit the problem of parameter estimation in computer vision, presented here as a simple optimization problem, considering (i) non-linear implicit measurement equations and parameter constraints, plus (ii) robust estimation in the presence of outliers and (iii) multi-model comparisons. Here, (1) a projection algorithm based on generalizations of square-root decompositions allows an efficient and numerically stable local resolution of a set of non-linear equations. On the other hand, (2) a robust estimation module of a hierarchy of non-linear models has been designed and validated. A step ahead, the software architecture of the estimation module is discussed with the goal of being integrated in reactive software environments or within applications with time constraints

Implementing a Variant of the Kanatani's Estimation Method

We revisit the problem of parameter estimation in computer vision, reconsideri- ng and implementing what may be called the Kanatani's estimation reconsidering and implementing what may be called the Kanatani's estimation method, presented here as a simple optimisation problem, so (a) without any method, presented here as a simple optimisation problem, so (a) without any direct reference to a probabilistic framework but (b) considering (i) direct reference to a probabilistic framework but (b) considering (i) non-linear implicit measurement equations and parameter constraints, plus non-linear implicit measurement equations and parameter constraints, plus (ii) robust estimation in the presence of outliers and (iii) multi-model (ii) robust estimation in the presence of outliers and (iii) multi-model comparisons. comparisons. Here, (A) a projection algorithm based on generalisations of square-root Here, (A) a projection algorithm based on generalisations of square-root decompositions allows an efficient and numerically stable local resolution decompositions allows an efficient and numerically stable local resolution of a set of non-linear equations. On the other hand, (B) a robust estimation of a set of non-linear equations. On the other hand, (B) a robust estimation module of a hierarchy of non-linear models has been designed and validated. module of a hierarchy of non-linear models has been designed and validated. A step ahead, (C) the software architecture of the estimation module is A step ahead, (C) the software architecture of the estimation module is discussed with the goal of being integrated in reactive software discussed with the goal of being integrated in reactive software environments or within applications with time constraints, while an environments or within applications with time constraints, while an experimentation considering the parameterisation of retinal displacements experimentation considering the parameterisation of retinal displacements between two views is proposed as an illustration of the estimation module

Relevé du patrimoine architectural par relevé laser, vision par ordinateur, et exploitation des règles architecturales

Le relevé laser donne la possibilité de scanner un grand nombre de points en peu de temps. Cependant, la création d'un modèle 3D à partir de ces données reste un travail important. Nous présentons une méthode pour faciliter cette tâche de modélisation. Notre approche est basée sur deux étapes. La première consiste à coupler le scanner laser à une caméra afin de guider le relevé par l'image. La deuxième étape consiste à modéliser les connaissances architecturales via une bibliothèque d'objets paramétrés