Les objets déformables sont omniprésents dans notre vie quotidienne. Chaque jour, nous manipulons des vêtements dans des configurations innombrables pour nous habiller, nouons les lacets de nos chaussures, cueillons des fruits et des légumes sans les endommager pour notre consommation et plions les reçus dans nos portefeuilles. Toutes ces tâches impliquent de manipuler des objets déformables et peuvent être exécutées sans problème par une personne. Toutefois, les robots n'ont pas encore atteint le même niveau de dextérité. Contrairement aux objets rigides, que les robots sont maintenant capables de manipuler avec des performances proches de celles des humains; les objets déformables doivent être contrôlés non seulement pour les positionner, mais aussi pour définir leur forme. Cette contrainte supplémentaire, relative au contrôle de la forme d’un objet, rend les techniques utilisées pour les objets rigides inapplicables aux objets déformables. En outre, le comportement des objets déformables diffère largement entre eux, par exemple: la forme d’un câble et des vêtements est considérablement affectée par la gravité, alors que celle-ci n’affecte pas la configuration d’autres objets déformables tels que des produits alimentaires. Ainsi, différentes approches ont été proposées pour des classes spécifiques d’objets déformables.Dans cette thèse, nous cherchons à remédier à ces lacunes en proposant une approche modulaire pour détecter la forme d'un objet pendant qu'il est manipulé par un robot. La modularité de cette approche s’inspire d’un paradigme de programmation qui s’applique de plus en plus au développement de logiciels en robotique et vise à apporter des solutions plus générales en séparant les fonctionnalités en composants. Ces composants peuvent ensuite être interchangés en fonction de la tâche ou de l'objet concerné. Cette stratégie est un moyen modulaire de suivre la forme d'objets déformables.Pour valider la stratégie proposée, nous avons implémenté trois applications différentes. Deux applications portaient exclusivement sur l'estimation de la déformation de l'objet à l'aide de données tactiles ou de données issues d’un capteur d’effort. La troisième application consistait à contrôler la déformation d'un objet. Une évaluation de la stratégie proposée, réalisée sur un ensemble d'objets élastiques pour les trois applications, montre des résultats prometteurs pour une approche qui n'utilise pas d'informations visuelles et qui pourrait donc être améliorée de manière significative par l'ajout de cette modalité. / Deformable objects are ubiquitous in our daily lives. On a given day, we manipulate clothes into uncountable configurations to dress ourselves, tie the shoelaces on our shoes, pick up fruits and vegetables without damaging them for our consumption and fold receipts into our wallets. All these tasks involve manipulating deformable objects and can be performed by an able person without any trouble, however robots have yet to reach the same level of dexterity. Unlike rigid objects, where robots are now capable of handling objects with close to human performance in some tasks; deformable objects must be controlled not only to account for their pose but also their shape. This extra constraint, to control an object's shape, renders techniques used for rigid objects mainly inapplicable to deformable objects. Furthermore, the behavior of deformable objects widely differs among them, e.g. the shape of a cable and clothes are significantly affected by gravity while it might not affect the configuration of other deformable objects such as food products. Thus, different approaches have been designed for specific classes of deformable objects.In this thesis we seek to address these shortcomings by proposing a modular approach to sense the shape of an object while it is manipulated by a robot. The modularity of the approach is inspired by a programming paradigm that has been increasingly been applied to software development in robotics and aims to achieve more general solutions by separating functionalities into components. These components can then be interchanged based on the specific task or object at hand. This provides a modular way to sense the shape of deformable objects.To validate the proposed pipeline, we implemented three different applications. Two applications focused exclusively on estimating the object's deformation using either tactile or force data, and the third application consisted in controlling the deformation of an object. An evaluation of the pipeline, performed on a set of elastic objects for all three applications, shows promising results for an approach that makes no use of visual information and hence, it could greatly be improved by the addition of this modality.

Determination of the relation between the bulk or rheological properties of a particle suspension and its microscopic structure is an old and important problem in physical science. In general, the rheology of particle suspension is quite complex, and the problem becomes even more complicated if the suspending particle is deformable. Despite these difficulties, a large number of theoretical and experimental investigations have been devoted to the analysis and prediction of the rheological behavior of particle suspensions. However, among these studies there are very few investigations that focus on the role of particle deformability. A novel method for full coupling of the fluid-solid phases with sub-grid accuracy for the solid phase is developed. In this method, the flow is computed on a fixed regular 'lattice' using the lattice Boltzmann method (LBM), where each solid particle, or fiber, is mapped onto a Lagrangian frame moving continuously through the domain. The motion and orientation of the particle are obtained from Newtonian dynamics equations. The deformable particle is modeled by the lattice-spring model (LSM).The fiber deformation is calculated by an efficient flexible fiber model. The no-slip boundary condition at the fluid-solid interface is based on the external boundary force (EBF) method. This method is validated by comparing with known experimental and theoretical results. The fiber simulation results show that the rheological properties of flexible fiber suspension are highly dependent on the microstructural characteristics of the suspension. It is shown that fiber stiffness (bending ratio BR) has strong impact on the suspension rheology in the range BR < 3. The relative viscosity of the fiber suspension under shear increases significantly as BR decreases. Direct numerical simulation of flexible fiber suspension allows computation of the primary normal stress difference as a function of BR. These results show that the primary normal stress difference has a minimum value at BR ∼ 1. The primary normal stress differences for slightly deformable fibers reaches a minimum and increases significantly as BR decreases below 1. The results are explained based on the Batchelor's relation for non-Brownian suspensions. The influence of fiber stiffness on the fiber orientation distribution and orbit constant is the major contributor to the variation in rheological properties. A least-squares curve-fitting relation for the relative viscosity is obtained for flexible fiber suspension. This relation can be used to predict the relative viscosity of flexible fiber suspension based on the result of rigid fiber suspension. The unique capability of the LBM-EBF method for sub-grid resolution and multiscale analysis of particle suspension is applied to the challenging problem of platelet motion in blood flow. By computing the stress distribution over the platelet, the "blood damage index" is computed and compared with experiments in channels with various geometries [43]. In platelet simulation, the effect of 3D channel geometry on the platelet activation and aggregation is modeled by using LBM-EBF method. Comparison of our simulations with Fallon's experiments [43] shows a similar pattern, and shows that Dumont's BDI model [40] is more appropriate for blood damage investigation. It has been shown that channels with sharp transition geometry will have larger recirculation areas with high BDI values. By investigating the effect of hinge area geometry on BDI value, we intend to use this multiscale computational method to optimize the design of Bileaflet mechanical heart valves. Both fiber simulations and platelet simulations have shown that the novel LBM-EBF method is more efficient and stable compare to the conventional numerical methods. The new EBF method is a two-Cway coupling method with sub-grid accuracy which makes the platelet simulations possible. The LBM-EBF is the only method to date, to the best of author's knowledge, that can simulate suspensions with large number of deformable particles under complex flow conditions. It is hoped that future researchers may benefit from this new method and the algorithms developed here.

Existing numerical techniques for modeling saturated deformable porous media are based on homogenization techniques and thus are incapable of performing micro-mechanical investigations, such as the effect of micro-structure on the deformational characteristics of the media. In this research work, a numerical scheme is developed based on the parallelized hybrid lattice-Boltzmann finite-element method, that is capable of performing micro-mechanical investigations through direct numerical simulations. The method has been used to simulate compression of model saturated porous media made of spheres and cylinders in regular arrangements. Through these simulations it is found that in the limit of small Reynolds number, Capillary number and strain, the deformational behaviour of a real porous media can be recovered through model porous media when the parameters porosity, permeability and bulk compressive modulus are matched between the two media. This finding motivated research in using model porous geometries to represent more complex real porous geometries in order to perform investigations of deformation on the latter. An attempt has been made to apply this technique to the complex geometries of ªfeltº, (a fibrous mat used in paper industries). These investigations lead to new understanding on the effect of fiber diameter on the bulk properties of a fibrous media and subsequently on the deformational behaviour of the media. Further the method has been used to investigate the constitutive relationships in deformable porous media. Particularly the relationship between permeability and porosity during the deformation of the media is investigated. Results show the need of geometry specific investigations.

Title from PDF of title page. / Document formatted into pages; contains 76 pages. / Thesis (M.S.E.E.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: The use of digitized information is rapidly gaining acceptance in bio-medical applications. Video compression plays an important role in the archiving and transmission of different digital diagnostic modalities. The present scheme of video compression for low bit-rate networks is not suitable for medical video sequences. The instability is the result of block artifacts resulting from the block based DCT coefficient quantization. The possibility of applying deformable motion estimation techniques to make the video compression standard (H.263) more adaptable for bio-medial applications was studied in detail. The study on the network characteristics and the behavior of various congestion control mechanisms was used to analyze the complete characteristics of existing low bit rate video compression algorithms. The study was conducted in three phases. The first phase involved the implementation and study of the present H.263 compression standard and its limitations. / ABSTRACT: The second phase dealt with the analysis of an external force for active contours which was used to obtain estimates for deformable objects. The external force, which is termed Gradient Vector Flow (GVF), was computed as a diffusion of the gradient vectors associated with a gray-level or binary edge map derived from the image. The mathematical aspect of a multi-scale framework based on a medial representation for the segmentation and shape characterization of anatomical objects in medical imagery was derived in detail. The medial representations were based on a hierarchical representation of linked figural models such as protrusions, indentations, neighboring figures and included figures--which represented solid regions and their boundaries. The third phase dealt with the vital parameters for effective video streaming over the internet in the bottleneck bandwidth, which gives the upper limit for the speed of data delivery from one end point to the other in a network. / ABSTRACT: If a codec attempts to send data beyond this limit, all packets above the limit will be lost. On the other hand, sending under this limit will clearly result in suboptimal video quality. During this phase the packet-drop-rate (PDR) performance of TCP(1/2) was investigated in conjunction with a few representative TCP-friendly congestion control protocols (CCP). The CCPs were TCP(1/256), SQRT(1/256) and TFRC (256), with and without self clocking. The CCPs were studied when subjected to an abrupt reduction in the available bandwidth. Additionally, the investigation studied the effect on the drop rates of TCP-Compatible algorithms by changing the queuing scheme from Random Early Detection (RED) to DropTail. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

Comment obtenir des données anatomiques pendant une neurochirurgie ? a été ce qui a guidé le travail développé dans le cadre de cette thèse. Les IRM sont actuellement utilisées en amont de l'opération pour fournir cette information, que ce soit pour le diagnostique ou pour définir le plan de traitement. De même, ces images pre-opératoires peuvent aussi être utilisées pendant l'opération, pour pallier la difficulté et le coût des images per-opératoires. Pour les rendre utilisables en salle d'opération, un recalage doit être effectué avec la position du patient. Cependant, le cerveau subit des déformations pendant la chirurgie, phénomène appelé Brain Shift, ce qui altère la qualité du recalage. Pour corriger cela, d'autres données per-opératoires peuvent être acquises, comme la localisation de la surface corticale, ou encore des images US localisées en 3D. Ce nouveau recalage permet de compenser ce problème, mais en partie seulement. Ainsi, des modèles mécaniques ont été développés, entre autres pour apporter des solutions à l'amélioration de ce recalage. Ils permettent ainsi d'estimer les déformations du cerveau. De nombreuses méthodes existent pour implémenter ces modèles, selon différentes lois de comportement et différents paramètres physiologiques. Dans tous les cas, cela requiert un modèle anatomique patient-spécifique. Actuellement, ce modèle est obtenu par contourage manuel, ou quelquefois semi-manuel. Le but de ce travail de thèse est donc de proposer une méthode automatique pour obtenir un modèle du cerveau adapté sur l'anatomie du patient, et utilisable pour une simulation mécanique. La méthode implémentée se base sur les modèles déformables pour segmenter les structures anatomiques les plus pertinentes dans une modélisation bio-mécanique. En effet, les membranes internes du cerveau sont intégrées: falx cerebri and tentorium cerebelli. Et bien qu'il ait été démontré que ces structures jouent un rôle primordial, peu d'études les prennent en compte. Par ailleurs, la segmentation résultante de notre travail est validée par comparaison avec des données disponibles en ligne. De plus, nous construisons un modèle 3D, dont les déformations seront simulées en utilisant une méthode de résolution par Éléments Finis. Ainsi, nous vérifions par des expériences l'importance des membranes, ainsi que celle des paramètres physiologiques.

Nonrigid deformation modeling and estimation from images is a technically challenging task due to its nonlinear, nonconvex and high-dimensional nature. Traditional optimization procedures often rely on good initializations and give locally optimal solutions. On the other hand, learning-based methods that directly model the relationship between deformed images and their parameters either cannot handle complicated forms of mapping, or suffer from the Nyquist Limit and the curse of dimensionality due to high degrees of freedom in the deformation space. In particular, to achieve a worst-case guarantee of ∈ error for a deformation with d degrees of freedom, the sample complexity required is O(1/∈d). In this thesis, a generative model for deformation is established and analyzed using a unified theoretical framework. Based on the framework, three algorithms, Data-Driven Descent, Top-down and Bottom-up Hierarchical Models, are designed and constructed to solve the generative model. Under Lipschitz conditions that rule out unsolvable cases (e.g., deformation of a blank image), all algorithms achieve globally optimal solutions to the specific generative model. The sample complexity of these methods is substantially lower than that of learning-based approaches, which are agnostic to deformation modeling. To achieve global optimality guarantees with lower sample complexity, the structureembedded in the deformation model is exploited. In particular, Data-driven Descentrelates two deformed images that are far away in the parameter space by compositionalstructures of deformation and reduce the sample complexity to O(Cd log 1/∈).Top-down Hierarchical Model factorizes the local deformation into patches once theglobal deformation has been estimated approximately and further reduce the samplecomplexity to O(Cd/1+C2 log 1/∈). Finally, the Bottom-up Hierarchical Model buildsrepresentations that are invariant to local deformation. With the representations, theglobal deformation can be estimated independently of local deformation, reducingthe sample complexity to O((C/∈)d0) (d0 ≪ d). From the analysis, this thesis showsthe connections between approaches that are traditionally considered to be of verydifferent nature. New theoretical conjectures on approaches like Deep Learning, arealso provided. practice, broad applications of the proposed approaches have also been demonstrated to estimate water distortion, air turbulence, cloth deformation and human pose with state-of-the-art results. Some approaches even achieve near real-time performance. Finally, application-dependent physics-based models are built with good performance in document rectification and scene depth recovery in turbulent media.

Even though the prospect of fusing images issued by different medical imagery systems is highly contemplated, the practical instantiation of it is subject to a theoretical hurdle: the definition of a similarity between images. Efforts in this field have proved successful for select pairs of images; however defining a suitable similarity between images regardless of their origin is one of the biggest challenges in deformable registration. In this thesis, we chose to develop generic approaches that allow the comparison of any two given modality. The recent advances in Machine Learning permitted us to provide innovative solutions to this very challenging problem. To tackle the problem of comparing incommensurable data we chose to view it as a data embedding problem where one embeds all the data in a common space in which comparison is possible. To this end, we explored the projection of one image space onto the image space of the other as well as the projection of both image spaces onto a common image space in which the comparison calculations are conducted. This was done by the study of the correspondences between image features in a pre-aligned dataset. In the pursuit of these goals, new methods for image regression as well as multi-modal metric learning methods were developed. The resulting learned similarities are then incorporated into a discrete optimization framework that mitigates the need for a differentiable criterion. Lastly we investigate on a new method that discards the constraint of a database of images that are pre-aligned, only requiring data annotated (segmented) by a physician. Experiments are conducted on two challenging medical images data-sets (Pre-Aligned MRI images and PET/CT images) to justify the benefits of our approach.

The purpose of this study is to develop an automatic facial feature extraction system, which is able to identify the detailed shape of eyes, eyebrows and mouth from facial images. The developed system not only extracts the location information of the features, but also estimates the parameters pertaining the contours and parts of the features using parametric deformable templates approach. In order to extract facial features, deformable models for each of eye, eyebrow, and mouth are developed. The development steps of the geometry, imaging model and matching algorithms, and energy functions for each of these templates are presented in detail, along with the important implementation issues. In addition, an eigenfaces based multi-scale face detection algorithm which incorporates standard facial proportions is implemented, so that when a face is detected the rough search regions for the facial features are readily available. The developed system is tested on JAFFE (Japanese Females Facial Expression Database), Yale Faces, and ORL (Olivetti Research Laboratory) face image databases. The performance of each deformable templates, and the face detection algorithm are discussed separately.

Cette thèse présente plusieurs contributions méthodologiques au traitement des images multicomposantes. Nous présentons notre travail dans le contexte applicatif difficile de l’imagerie de tomographie d’émission de positons dynamique (TEPd), une modalité d’imagerie fonctionnelle produisant des images multicomposantes fortement dégradées. Le caractère vectoriel du signal offre des propriétés de redondance et de complémentarité de l’information le long des différentes composantes permettant d’en améliorer le traitement. Notre première contribution exploite cet avantage pour la segmentation robuste de volumes d’intérêt au moyen de modèles déformables. Nous proposons un champ de forces extérieures guidant les modèles déformables vers les contours vectoriels des régions à délimiter. Notre seconde contribution porte sur la restauration de telles images pour faciliter leur traitement ultérieur. Nous proposons une nouvelle méthode de restauration par équations aux dérivées partielles permettant d’augmenter le rapport signal sur bruit d’images dégradées et d’en renforcer la netteté. Appliqués à l’imagerie TEPd, nous montrons l’apport de nos contributions pour un problème ouvert des neurosciences, la quantification non invasive d’un radiotraceur de la neuroinflammation. / This thesis presents several methodological contributions to the processing of vector-valued images, with dynamic positron emission tomography imaging (dPET) as its target application. dPET imaging is a functional imaging modality that produces highly degraded images composed of subsequent temporal acquisitions. Vector-valued images often present some level of redundancy or complementarity of information along the channels, allowing the enhancement of processing results. Our first contribution exploits such properties for performing robust segmentation of target volumes with deformable models.We propose a new external force field to guide deformable models toward the vector edges of regions of interest. Our second contribution deals with the restoration of such images to further facilitate their analysis. We propose a new partial differential equation-based approach that enhances the signal to noise ratio of degraded images while sharpening their edges. Applied to dPET imaging, we show to what extent our methodological contributions can help to solve an open problem in neuroscience : noninvasive quantification of neuroinflammation.

Les contours actifs sont parmi les méthodes de segmentation d'images les plus utilisées et de nombreuses implémentations ont vu le jour durant ces 25 dernières années. Parmi elles, l'approche greedy est considérée comme l'une des plus rapides et des plus stables. Toutefois, quelle que soit l'implémentation choisie, les résultats de segmentation souffrent grandement en présence d'occlusions, de concavités ou de déformation anormales de la forme. Si l'on dispose d'informations a priori sur la forme recherchée, alors son incorporation à un modèle existant peut permettre d'améliorer très nettement les résultats de segmentation. Dans cette thèse, l'inclusion de ce type de contraintes de formes dans un modèle de contour actif explicite est proposée. Afin de garantir une invariance à la rotation, à la translation et au changement d'échelle, les descripteurs de Fourier sont utilisés. Contrairement à la plupart des méthodes existantes, qui comparent la forme de référence et le contour actif en cours d'évolution dans le domaine d'origine par le biais d'une transformation inverse, la méthode proposée ici réalise cette comparaison dans l'espace des descripteurs. Cela assure à notre approche un faible temps de calcul et lui permet d'être indépendante du nombre de points de contrôle choisis pour le contour actif. En revanche, cela induit un biais dans la phase des coefficients de Fourier, handicapant l'invariance à la rotation. Ce problème est résolu par un algorithme original. Les expérimentations indiquent clairement que l'utilisation de ce type de contrainte de forme améliore significativement les résultats de segmentation du modèle de contour actif utilisé. / Active contours are widely used for image segmentation. There are many implementations of active contours. The greedy algorithm is being regarded as one of the fastest and stable implementations. No matter which implementation is being employed, the segmentation results suffer greatly in the presence of occlusion, context noise, concavities or abnormal deformation of shape. If some prior knowledge about the shape of the object is available, then its addition to an existing model can greatly improve the segmentation results. In this thesis inclusion of such shape constraints for explicit active contours is being implemented. These shape priors are introduced through the use of robust Fourier based descriptors which makes them invariant to the translation, scaling and rotation factors and enables the deformable model to converge towards the prior shape even in the presence of occlusion and contextual noise. Unlike most existing methods which compare the reference shape and evolving contour in the spatial domain by applying the inverse transforms, our proposed method realizes such comparisons entirely in the descriptor space. This not only decreases the computational time but also allows our method to be independent of the number of control points chosen for the description of the active contour. This formulation however, may introduce certain anomalies in the phase of the descriptors which affects the rotation invariance. This problem has been solved by an original algorithm. Experimental results clearly indicate that the inclusion of these shape priors significantly improved the segmentation results of the active contour model being used.