Applications de l'estimation de la variation des échelles locales d'un plan texturé

Les méthodes de "Shape From Texture (SFT)" permettent de retrouver l'orientation d'un plan texturé incliné à partir d'une image unique de ce plan. L'objectif de cet article est de montrer l'apport d'une de ces méthodes pour quatre applications : segmentation des texels, construction d'un maillage, extraction de défauts, calcul de l'orientation à partir de la ligne de fuite. La méthode de "SFT" développée est basée sur une analyse de la variation des échelles locales. Elle est articulée en deux parties : extraction des échelles locales puis interpolation de la carte des échelles obtenues avec la surface théorique afin de calculer directement l'orientation du plan ou d'utiliser ces résultats pour les applications visées

From uncertainty to adaptivity : multiscale edge detection and image segmentation

This thesis presents the research on two different tasks in computer vision: edge detection and image segmentation (including texture segmentation and motion field segmentation). The central issue of this thesis is the uncertainty of the joint space-frequency image analysis, which motivates the design of the adaptive multiscale/multiresolution schemes for edge detection and image segmentation. Edge detectors capture most of the local features in an image, including the object boundaries and the details of surface textures. Apart from these edge features, the region properties of surface textures and motion fields are also important for segmenting an image into disjoint regions. The major theoretical achievements of this thesis are twofold. First, a scale parameter for the local processing of an image (e.g. edge detection) is proposed. The corresponding edge behaviour in the scale space, referred to as Bounded Diffusion, is the basis of a multiscale edge detector where the scale is adjusted adaptively according to the local noise level. Second, an adaptive multiresolution clustering scheme is proposed for texture segmentation (referred to as Texture Focusing) and motion field segmentation. In this scheme, the central regions of homogeneous textures (motion fields) are analysed using coarse resolutions so as to achieve a better estimation of the textural content (optical flow), and the border region of a texture (motion field) is analysed using fine resolutions so as to achieve a better estimation of the boundary between textures (moving objects). Both of the above two achievements are the logical consequences of the uncertainty principle. Four algorithms, including a roof edge detector, a multiscale step edge detector, a texture segmentation scheme and a motion field segmentation scheme are proposed to address various aspects of edge detection and image segmentation. These algorithms have been implemented and extensively evaluated

Orientation computation of an inclined textured plane: accuracy and performances

The aim of this paper is to present one method for computing the orientation of an inclined textured plane with only one view of this plane. Two steps are used for this computation. First we build a local scales map by a wavelets decomposition of the image of the plane. Then we have to do an interpolation of this map by use the theoretical equation of the local scales variation. So we obtain features values which allow us to compute the tilt and the slant angles. After developing the computation technique, we do a theoretical study in order to know the precision of the method. For the tilt angle, the precision is about one degree, but for the slant angle the precision is only about five degrees, if the slant angle is over forty degrees. But, we have to know the camera parameters for computing the slant angle. If there is some errors about these parameters, so the slant angle will be bad. After this study, we build a data base of one hundred images of real textures with different tilt and slant angles. The camera which has been used for acquiring the images has been calibrated. Results on this data base are agree with the theoretical study.Le but de cet article est de présenter une méthode de calcul de l'orientation d'un plan texturé incliné à partir d'une seule vue de ce plan. Cette méthode est constituée de deux étapes. Dans un premier temps on calcule, à partir de l'image initiale, une carte des échelles locales. Ces échelles sont obtenues au moyen d'une décomposition en ondelettes de l'image d'origine. Puis on interpole cette carte des échelles locales par l'équation théorique de leurs variations. On obtient ainsi des paramètres qui permettent de calculer les angles de tilt et de slant, décrivant l'orientation du plan. Pour valider cette démarche, nous avons mené une étude théorique sur la précision qui pouvait être atteinte par une telle méthode. Nous avons pu mettre en évidence que, si la précision sur l'angle de tilt était assez bonne (de l'ordre de 1°), celle sur l'angle de slant n'excédait pas 5°, à condition que cet angle soit suffisamment important (supérieur à 40°). Mais la précision sur l'angle de slant est conditionnée par la connaissance des paramètres de prise de vue. En effet, nous avons mis en évidence que l'utilisation de valeurs erronées des paramètres de la caméra entraînerait une erreur maximum pour un slant entre 40° et 50°, c'est à dire, a priori, là où la méthode est la meilleure. Cette étude théorique a été validée par des expérimentations sur des images de synthèse et sur des images de textures réelles. Une base de données d'une centaine d'images a été constituée, au moyen d'une caméra préalablement calibrée, pour évaluer la qualité des résultats fournis par notre méthode

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Physically-based simulation of ice formation

The geometric and optical complexity of ice has been a constant source of wonder and inspiration for scientists and artists. It is a defining seasonal characteristic, so modeling it convincingly is a crucial component of any synthetic winter scene. Like wind and fire, it is also considered elemental, so it has found considerable use as a dramatic tool in visual effects. However, its complex appearance makes it difficult for an artist to model by hand, so physically-based simulation methods are necessary. In this dissertation, I present several methods for visually simulating ice formation. A general description of ice formation has been known for over a hundred years and is referred to as the Stefan Problem. There is no known general solution to the Stefan Problem, but several numerical methods have successfully simulated many of its features. I will focus on three such methods in this dissertation: phase field methods, diffusion limited aggregation, and level set methods. Many different variants of the Stefan problem exist, and each presents unique challenges. Phase field methods excel at simulating the Stefan problem with surface tension anisotropy. Surface tension gives snowflakes their characteristic six arms, so phase field methods provide a way of simulating medium scale detail such as frost and snowflakes. However, phase field methods track the ice as an implicit surface, so it tends to smear away small-scale detail. In order to restore this detail, I present a hybrid method that combines phase fields with diffusion limited aggregation (DLA). DLA is a fractal growth algorithm that simulates the quasi-steady state, zero surface tension Stefan problem, and does not suffer from smearing problems. I demonstrate that combining these two algorithms can produce visual features that neither method could capture alone. Finally, I present a method of simulating icicle formation. Icicle formation corresponds to the thin-film, quasi-steady state Stefan problem, and neither phase fields nor DLA are directly applicable. I instead use level set methods, an alternate implicit front tracking strategy. I derive the necessary velocity equations for level set simulation, and also propose an efficient method of simulating ripple formation across the surface of the icicles