Análise de Movimento Não Rígido em Visão por Computador

Neste artigo são apresentadas várias metodologias actualmente existentes, no domínio da Visão por Computador, para a análise de movimento não rígido e são indicados diversos exemplos de aplicações. Assim o movimento não rígido é classificado e, para cada classe resultante, são indicadas as restrições e as condições inerentes e verificados alguns trabalhos realizados no seu âmbito. Como as questões de análise de movimento e modelização da forma se tornam inseparáveis quando se considera o movimento do tipo não rígido, a modelização sugere uma classificação possível da forma não rígida e do movimento. Assim são também apresentados modelos de forma para objectos deformáveis e indicados vários exemplos de aplicações. Com este estudo, de certo modo aprofundado, das várias metodologias, e suas aplicações, existentes no domínio da análise de movimento não rígido, espera-se contribuir para o seu desenvolvimento, dada a actual carência de boas revisões do estado da arte neste domínio.In this article several methodologies actually existent, in the Computer Vision domain, for non-rigid movement analysis are presented and several examples of applications are indicated. Thus the non-rigid movement is classified and, for each resulting class, the restrictions and the inherent conditions are presented and some works accomplished in its ambit are verified. As the questions of movement and shape analysis becomes non-separable when its considered the movement of the non-rigid type, the shape models also suggests a possible classification of the non-rigid shape and of the movement. Thus shape models for deformable objects will be presented and some examples of applications indicated. With this study, in certain way deep, of several methodologies, and its applications, existent in the domain of the non-rigid movement analysis, the authors hope to contribute for its development, given the actual lack of good state of the art revisions in this domain

Comparing Features of Three-Dimensional Object Models Using Registration Based on Surface Curvature Signatures

This dissertation presents a technique for comparing local shape properties for similar three-dimensional objects represented by meshes. Our novel shape representation, the curvature map, describes shape as a function of surface curvature in the region around a point. A multi-pass approach is applied to the curvature map to detect features at different scales. The feature detection step does not require user input or parameter tuning. We use features ordered by strength, the similarity of pairs of features, and pruning based on geometric consistency to efficiently determine key corresponding locations on the objects. For genus zero objects, the corresponding locations are used to generate a consistent spherical parameterization that defines the point-to-point correspondence used for the final shape comparison

Modeling and real-time rendering of participating media using the GPU

Cette thèse traite de la modélisation, l'illumination et le rendu temps-réel de milieux participants à l'aide du GPU. Dans une première partie, nous commençons par développer une méthode de rendu de nappes de brouillard hétérogènes pour des scènes en extérieur. Le brouillard est modélisé horizontalement dans une base 2D de fonctions de Haar ou de fonctions B-Spline linéaires ou quadratiques, dont les coefficients peuvent être chargés depuis une textit{fogmap}, soit une carte de densité en niveaux de gris. Afin de donner au brouillard son épaisseur verticale, celui-ci est doté d'un coefficient d'atténuation en fonction de l'altitude, utilisé pour paramétrer la rapidité avec laquelle la densité diminue avec la distance au milieu selon l'axe Y. Afin de préparer le rendu temps-réel, nous appliquons une transformée en ondelettes sur la carte de densité du brouillard, afin d'en extraire une approximation grossière (base de fonctions B-Spline) et une série de couches de détails (bases d'ondelettes B-Spline), classés par fréquence.%Les détails sont ainsi classés selon leur fréquence et, additionnées, permettent de retrouver la carte de densité d'origine. Chacune de ces bases de fonctions 2D s'apparente à une grille de coefficients. Lors du rendu sur GPU, chacune de ces grilles est traversée pas à pas, case par case, depuis l'observateur jusqu'à la plus proche surface solide. Grâce à notre séparation des différentes fréquences de détails lors des pré-calculs, nous pouvons optimiser le rendu en ne visualisant que les détails les plus contributifs visuellement en avortant notre parcours de grille à une distance variable selon la fréquence. Nous présentons ensuite d'autres travaux concernant ce même type de brouillard : l'utilisation de la transformée en ondelettes pour représenter sa densité via une grille non-uniforme, la génération automatique de cartes de densité et son animation à base de fractales, et enfin un début d'illumination temps-réel du brouillard en simple diffusion. Dans une seconde partie, nous nous intéressons à la modélisation, l'illumination en simple diffusion et au rendu temps-réel de fumée (sans simulation physique) sur GPU. Notre méthode s'inspire des Light Propagation Volumes (volume de propagation de lumière), une technique à l'origine uniquement destinée à la propagation de la lumière indirecte de manière complètement diffuse, après un premier rebond sur la géométrie. Nous l'adaptons pour l'éclairage direct, et l'illumination des surfaces et milieux participants en simple diffusion. Le milieu est fourni sous forme d'un ensemble de bases radiales (blobs), puis est transformé en un ensemble de voxels, ainsi que les surfaces solides, de manière à disposer d'une représentation commune. Par analogie aux LPV, nous introduisons un Occlusion Propagation Volume, dont nous nous servons, pour calculer l'intégrale de la densité optique entre chaque source et chaque autre cellule contenant soit un voxel du milieu, soit un voxel issu d'une surface. Cette étape est intégrée à la boucle de rendu, ce qui permet d'animer le milieu participant ainsi que les sources de lumière sans contrainte particulière. Nous simulons tous types d'ombres : dues au milieu ou aux surfaces, projetées sur le milieu ou les surfacesThis thesis deals with modeling, illuminating and rendering participating media in real-time using graphics hardware. In a first part, we begin by developing a method to render heterogeneous layers of fog for outdoor scenes. The medium is modeled horizontally using a 2D Haar or linear/quadratic B-Spline function basis, whose coefficients can be loaded from a fogmap, i.e. a grayscale density image. In order to give to the fog its vertical thickness, it is provided with a coefficient parameterizing the extinction of the density when the altitude to the fog increases. To prepare the rendering step, we apply a wavelet transform on the fog's density map, and extract a coarse approximation and a series of layers of details (B-Spline wavelet bases).These details are ordered according to their frequency and, when summed back together, can reconstitute the original density map. Each of these 2D function basis can be viewed as a grid of coefficients. At the rendering step on the GPU, each of these grids is traversed step by step, cell by cell, since the viewer's position to the nearest solid surface. Thanks to our separation of the different frequencies of details at the precomputations step, we can optimize the rendering by only visualizing details that contribute most to the final image and abort our grid traversal at a distance depending on the grid's frequency. We then present other works dealing with the same type of fog: the use of the wavelet transform to represent the fog's density in a non-uniform grid, the automatic generation of density maps and their animation based on Julia fractals, and finally a beginning of single-scattering illumination of the fog, where we are able to simulate shadows by the medium and the geometry. In a second time, we deal with modeling, illuminating and rendering full 3D single-scattering sampled media such as smoke (without physical simulation) on the GPU. Our method is inspired by light propagation volumes, a technique whose only purpose was, at the beginning, to propagate fully diffuse indirect lighting. We adapt it to direct lighting, and the illumination of both surfaces and participating media. The medium is provided under the form of a set of radial bases (blobs), and is then transformed into a set of voxels, together with solid surfaces, so that both entities can be manipulated more easily under a common form. By analogy to the LPV, we introduce an occlusion propagation volume, which we use to compute the integral of the optical density, between each source and each other cell containing a voxel either generated from the medium, or from a surface. This step is integrated into the rendering process, which allows to animate participating media and light sources without any further constraintPARIS-EST-Université (770839901) / SudocSudocFranceF

Doctor of Philosophy

dissertationVolumetric parameterization is an emerging field in computer graphics, where volumetric representations that have a semi-regular tensor-product structure are desired in applications such as three-dimensional (3D) texture mapping and physically-based simulation. At the same time, volumetric parameterization is also needed in the Isogeometric Analysis (IA) paradigm, which uses the same parametric space for representing geometry, simulation attributes and solutions. One of the main advantages of the IA framework is that the user gets feedback directly as attributes of the NURBS model representation, which can represent geometry exactly, avoiding both the need to generate a finite element mesh and the need to reverse engineer the simulation results from the finite element mesh back into the model. Research in this area has largely been concerned with issues of the quality of the analysis and simulation results assuming the existence of a high quality volumetric NURBS model that is appropriate for simulation. However, there are currently no generally applicable approaches to generating such a model or visualizing the higher order smooth isosurfaces of the simulation attributes, either as a part of current Computer Aided Design or Reverse Engineering systems and methodologies. Furthermore, even though the mesh generation pipeline is circumvented in the concept of IA, the quality of the model still significantly influences the analysis result. This work presents a pipeline to create, analyze and visualize NURBS geometries. Based on the concept of analysis-aware modeling, this work focusses in particular on methodologies to decompose a volumetric domain into simpler pieces based on appropriate midstructures by respecting other relevant interior material attributes. The domain is decomposed such that a tensor-product style parameterization can be established on the subvolumes, where the parameterization matches along subvolume boundaries. The volumetric parameterization is optimized using gradient-based nonlinear optimization algorithms and datafitting methods are introduced to fit trivariate B-splines to the parameterized subvolumes with guaranteed order of accuracy. Then, a visualization method is proposed allowing to directly inspect isosurfaces of attributes, such as the results of analysis, embedded in the NURBS geometry. Finally, the various methodologies proposed in this work are demonstrated on complex representations arising in practice and research

Improving the forward model for electrical impedance tomography of brain function through rapid generation of subject specific finite element models

Electrical Impedance Tomography (EIT) is a non-invasive imaging method which allows internal electrical impedance of any conductive object to be imaged by means of current injection and surface voltage measurements through an array of externally applied electrodes. The successful generation of the image requires the simulation of the current injection patterns on either an analytical or a numerical model of the domain under examination, known as the forward model, and using the resulting voltage data in the inverse solution from which images of conductivity changes can be constructed. Recent research strongly indicates that geometric and anatomical conformance of the forward model to the subject under investigation significantly affects the quality of the images. This thesis focuses mainly on EIT of brain function and describes a novel approach for the rapid generation of patient or subject specific finite element models for use as the forward model. After introduction of the topic, methods of generating accurate finite element (FE) models using commercially available Computer-Aided Design (CAD) tools are described and show that such methods, though effective and successful, are inappropriate for time critical clinical use. The feasibility of warping or morphing a finite element mesh as a means of reducing the lead time for model generation is then presented and demonstrated. This leads on to the description of methods of acquiring and utilising known system geometry, namely the positions of electrodes and registration landmarks, to construct an accurate surface of the subject, the results of which are successfully validated. The outcome of this procedure is then used to specify boundary conditions to a mesh warping algorithm based on elastic deformation using well-established continuum mechanics procedures. The algorithm is applied to a range of source models to empirically establish optimum values for the parameters defining the problem which can successfully generate meshes of acceptable quality in terms of discretization errors and which more accurately define the geometry of the target subject. Further validation of the algorithm is performed by comparison of boundary voltages and image reconstructions from simulated and laboratory data to demonstrate that benefits in terms of image artefact reduction and localisation of conductivity changes can be gained. The processes described in the thesis are evaluated and discussed and topics of further work and application are described

Análise de Movimento Não Rígido em Visão por Computador

Neste artigo são apresentadas várias metodologias actualmente existentes, no domínio da Visão por Computador, para a análise de movimento não rígido e são indicados diversos exemplos de aplicações. Assim o movimento não rígido é classificado e, para cada classe resultante, são indicadas as restrições e as condições inerentes e verificados alguns trabalhos realizados no seu âmbito. Como as questões de análise de movimento e modelização da forma se tornam inseparáveis quando se considera o movimento do tipo não rígido, a modelização sugere uma classificação possível da forma não rígida e do movimento. Assim são também apresentados modelos de forma para objectos deformáveis e indicados vários exemplos de aplicações. Com este estudo, de certo modo aprofundado, das várias metodologias, e suas aplicações, existentes no domínio da análise de movimento não rígido, espera-se contribuir para o seu desenvolvimento, dada a actual carência de boas revisões do estado da arte neste domínio

Phenomenological modeling of image irradiance for non-Lambertian surfaces under natural illumination.

Various vision tasks are usually confronted by appearance variations due to changes of illumination. For instance, in a recognition system, it has been shown that the variability in human face appearance is owed to changes to lighting conditions rather than person\u27s identity. Theoretically, due to the arbitrariness of the lighting function, the space of all possible images of a fixed-pose object under all possible illumination conditions is infinite dimensional. Nonetheless, it has been proven that the set of images of a convex Lambertian surface under distant illumination lies near a low dimensional linear subspace. This result was also extended to include non-Lambertian objects with non-convex geometry. As such, vision applications, concerned with the recovery of illumination, reflectance or surface geometry from images, would benefit from a low-dimensional generative model which captures appearance variations w.r.t. illumination conditions and surface reflectance properties. This enables the formulation of such inverse problems as parameter estimation. Typically, subspace construction boils to performing a dimensionality reduction scheme, e.g. Principal Component Analysis (PCA), on a large set of (real/synthesized) images of object(s) of interest with fixed pose but different illumination conditions. However, this approach has two major problems. First, the acquired/rendered image ensemble should be statistically significant vis-a-vis capturing the full behavior of the sources of variations that is of interest, in particular illumination and reflectance. Second, the curse of dimensionality hinders numerical methods such as Singular Value Decomposition (SVD) which becomes intractable especially with large number of large-sized realizations in the image ensemble. One way to bypass the need of large image ensemble is to construct appearance subspaces using phenomenological models which capture appearance variations through mathematical abstraction of the reflection process. In particular, the harmonic expansion of the image irradiance equation can be used to derive an analytic subspace to represent images under fixed pose but different illumination conditions where the image irradiance equation has been formulated in a convolution framework. Due to their low-frequency nature, irradiance signals can be represented using low-order basis functions, where Spherical Harmonics (SH) has been extensively adopted. Typically, an ideal solution to the image irradiance (appearance) modeling problem should be able to incorporate complex illumination, cast shadows as well as realistic surface reflectance properties, while moving away from the simplifying assumptions of Lambertian reflectance and single-source distant illumination. By handling arbitrary complex illumination and non-Lambertian reflectance, the appearance model proposed in this dissertation moves the state of the art closer to the ideal solution. This work primarily addresses the geometrical compliance of the hemispherical basis for representing surface reflectance while presenting a compact, yet accurate representation for arbitrary materials. To maintain the plausibility of the resulting appearance, the proposed basis is constructed in a manner that satisfies the Helmholtz reciprocity property while avoiding high computational complexity. It is believed that having the illumination and surface reflectance represented in the spherical and hemispherical domains respectively, while complying with the physical properties of the surface reflectance would provide better approximation accuracy of image irradiance when compared to the representation in the spherical domain. Discounting subsurface scattering and surface emittance, this work proposes a surface reflectance basis, based on hemispherical harmonics (HSH), defined on the Cartesian product of the incoming and outgoing local hemispheres (i.e. w.r.t. surface points). This basis obeys physical properties of surface reflectance involving reciprocity and energy conservation. The basis functions are validated using analytical reflectance models as well as scattered reflectance measurements which might violate the Helmholtz reciprocity property (this can be filtered out through the process of projecting them on the subspace spanned by the proposed basis, where the reciprocity property is preserved in the least-squares sense). The image formation process of isotropic surfaces under arbitrary distant illumination is also formulated in the frequency space where the orthogonality relation between illumination and reflectance bases is encoded in what is termed as irradiance harmonics. Such harmonics decouple the effect of illumination and reflectance from the underlying pose and geometry. Further, a bilinear approach to analytically construct irradiance subspace is proposed in order to tackle the inherent problem of small-sample-size and curse of dimensionality. The process of finding the analytic subspace is posed as establishing a relation between its principal components and that of the irradiance harmonics basis functions. It is also shown how to incorporate prior information about natural illumination and real-world surface reflectance characteristics in order to capture the full behavior of complex illumination and non-Lambertian reflectance. The use of the presented theoretical framework to develop practical algorithms for shape recovery is further presented where the hitherto assumed Lambertian assumption is relaxed. With a single image of unknown general illumination, the underlying geometrical structure can be recovered while accounting explicitly for object reflectance characteristics (e.g. human skin types for facial images and teeth reflectance for human jaw reconstruction) as well as complex illumination conditions. Experiments on synthetic and real images illustrate the robustness of the proposed appearance model vis-a-vis illumination variation. Keywords: computer vision, computer graphics, shading, illumination modeling, reflectance representation, image irradiance, frequency space representations, {hemi)spherical harmonics, analytic bilinear PCA, model-based bilinear PCA, 3D shape reconstruction, statistical shape from shading