Les méthodes de recalage d’images, qui ont pour but l’alignement de deux ou plusieurs images dans un même système de coordonnées, sont parmi les algorithmes les plus anciens et les plus utilisés en vision par ordinateur. Les méthodes de recalage servent à établir des correspondances entre des images (prises à des moments différents, par différents senseurs ou avec différentes perspectives), lesquelles ne sont pas évidentes pour l’œil humain. Un type particulier d’algorithme de recalage, connu comme « les méthodes de recalage déformables à l’aide de modèles graphiques » est devenu de plus en plus populaire ces dernières années, grâce à sa robustesse, sa scalabilité, son efficacité et sa simplicité théorique. La gamme des problèmes auxquels ce type d’algorithme peut être adapté est particulièrement vaste. Dans ce travail de thèse, nous proposons plusieurs extensions à la théorie de recalage déformable à l’aide de modèles graphiques, en explorant de nouvelles applications et en développant des contributions méthodologiques originales.Notre première contribution est une extension du cadre du recalage à l’aide de graphes, en abordant le problème très complexe du recalage d’une tranche avec un volume. Le recalage d’une tranche avec un volume est le recalage 2D dans un volume 3D, comme par exemple le mapping d’une tranche tomographique dans un système de coordonnées 3D d’un volume en particulier. Nos avons proposé une formulation scalable, modulaire et flexible pour accommoder des termes d'ordre élevé et de rang bas, qui peut sélectionner le plan et estimer la déformation dans le plan de manière simultanée par une seule approche d'optimisation. Le cadre proposé est instancié en différentes variantes, basés sur différentes topologies du graph, définitions de l'espace des étiquettes et constructions de l'énergie. Le potentiel de notre méthode a été démontré sur des données réelles ainsi que des données simulées dans le cadre d’une résonance magnétique d’ultrason (où le cadre d’installation et les stratégies d’optimisation ont été considérés).Les deux autres contributions inclues dans ce travail de thèse, sont liées au problème de l’intégration de l’information sémantique dans la procédure de recalage (indépendamment de la dimensionnalité des images). Actuellement, la plupart des méthodes comprennent une seule fonction métrique pour expliquer la similarité entre l’image source et l’image cible. Nous soutenons que l'intégration des informations sémantiques pour guider la procédure de recalage pourra encore améliorer la précision des résultats, en particulier en présence d'étiquettes sémantiques faisant du recalage un problème spécifique adapté à chaque domaine.Nous considérons un premier scénario en proposant un classificateur pour inférer des cartes de probabilité pour les différentes structures anatomiques dans les images d'entrée. Notre méthode vise à recaler et segmenter un ensemble d'images d'entrée simultanément, en intégrant cette information dans la formulation de l'énergie. L'idée principale est d'utiliser ces cartes estimées des étiquettes sémantiques (fournie par un classificateur arbitraire) comme un substitut pour les données non-étiquettées, et les combiner avec le recalage déformable pour améliorer l'alignement ainsi que la segmentation.Notre dernière contribution vise également à intégrer l'information sémantique pour la procédure de recalage, mais dans un scénario différent. Dans ce cas, au lieu de supposer que nous avons des classificateurs arbitraires pré-entraînés à notre disposition, nous considérons un ensemble d’annotations précis (vérité terrain) pour une variété de structures anatomiques. Nous présentons une contribution méthodologique qui vise à l'apprentissage des critères correspondants au contexte spécifique comme une agrégation des mesures de similarité standard à partir des données annotées, en utilisant une adaptation de l’algorithme « Latent Structured Support Vector Machine ». / Image registration methods, which aim at aligning two or more images into one coordinate system, are among the oldest and most widely used algorithms in computer vision. Registration methods serve to establish correspondence relationships among images (captured at different times, from different sensors or from different viewpoints) which are not obvious for the human eye. A particular type of registration algorithm, known as graph-based deformable registration methods, has become popular during the last decade given its robustness, scalability, efficiency and theoretical simplicity. The range of problems to which it can be adapted is particularly broad. In this thesis, we propose several extensions to the graph-based deformable registration theory, by exploring new application scenarios and developing novel methodological contributions.Our first contribution is an extension of the graph-based deformable registration framework, dealing with the challenging slice-to-volume registration problem. Slice-to-volume registration aims at registering a 2D image within a 3D volume, i.e. we seek a mapping function which optimally maps a tomographic slice to the 3D coordinate space of a given volume. We introduce a scalable, modular and flexible formulation accommodating low-rank and high order terms, which simultaneously selects the plane and estimates the in-plane deformation through a single shot optimization approach. The proposed framework is instantiated into different variants based on different graph topology, label space definition and energy construction. Simulated and real-data in the context of ultrasound and magnetic resonance registration (where both framework instantiations as well as different optimization strategies are considered) demonstrate the potentials of our method.The other two contributions included in this thesis are related to how semantic information can be encompassed within the registration process (independently of the dimensionality of the images). Currently, most of the methods rely on a single metric function explaining the similarity between the source and target images. We argue that incorporating semantic information to guide the registration process will further improve the accuracy of the results, particularly in the presence of semantic labels making the registration a domain specific problem.We consider a first scenario where we are given a classifier inferring probability maps for different anatomical structures in the input images. Our method seeks to simultaneously register and segment a set of input images, incorporating this information within the energy formulation. The main idea is to use these estimated maps of semantic labels (provided by an arbitrary classifier) as a surrogate for unlabeled data, and combine them with population deformable registration to improve both alignment and segmentation.Our last contribution also aims at incorporating semantic information to the registration process, but in a different scenario. In this case, instead of supposing that we have pre-trained arbitrary classifiers at our disposal, we are given a set of accurate ground truth annotations for a variety of anatomical structures. We present a methodological contribution that aims at learning context specific matching criteria as an aggregation of standard similarity measures from the aforementioned annotated data, using an adapted version of the latent structured support vector machine (LSSVM) framework.

No description available.

Object deformation is an essential factor for the robot to manipulate the object, as the deformation impacts the grasping of the deformable object either positively or negatively. One of the most challenging problems with deformable objects is estimating the stiffness parameters such as Young’s modulus and Poisson’s ratio. This thesis presents a learning-based approach to predicting the stiffness parameters of a 3D (volumetric) deformable object based on vision and haptic feedback. A deep learning network is designed to predict Young’s modulus of homogeneous isotropic deformable objects from the forces of squeezing the object and the depth images of the deformed part of the object. The results show that the developed method can estimate Young’s modulus of the selected synthetic objects in the validation samples dataset with 3.017% error upper bound on the 95% confidence interval. The conclusion is that this method contributes to predicting Young’s modulus of the homogeneous isotropic objects in the simulation environments. In future work, the diversity of the object shape samples can be expanded for broader application in predicting Young’s modulus. Besides, the method can also be extended to real-world objects after validating real-world experiments. / Objekt är en väsentlig faktor för roboten att manipulera objektet, eftersom det påverkar greppet om det deformerbara objektets deformation antingen positivt eller negativt. Ett av de mest utmanande problemen med deformerbara objekt är att uppskatta styvhetsparametrarna som Youngs modul och Poissons förhållande . Denna avhandling presenterar en inlärningsbaserad metod för att förutsäga styvhetsparametrarna för ett 3D (volumetriskt) deformerbart objekt baserat på syn och haptisk feedback. Ett nätverk för djupinlärning är utformat för att förutsäga Youngs modul av homogena isotropa deformerbara objekt från krafterna från att klämma ihop objektet och djupbilderna av den deformerade delen av objektet Resultaten visar att den utvecklade metoden kan uppskatta Youngs modul för de utvalda syntetiska objekten i valideringsexempeldatauppsättningen med 3.017% fel övre gräns på 95% konfidensintervall. Slutsatsen är att denna metod bidrar till att förutsäga Youngs modul för de homogena isotropa objekten i simuleringsmiljöerna. I framtida bredare arbete kan mångfalden av objektformproverna utökas för tillämpning vid förutsägelse av Youngs modul. Dessutom kan metoden också utvidgas till verkliga objekt efter validering av verkliga experiment.

No description available.

<p>This thesis presents a computational model of the surgical stitching tasks and a path planning algorithm for robotic assisted stitching. The overall goal of the research is to enable surgical robots to perform automatic suturing. Suturing comprises several distinct steps, one of them is the stitching. During stitching, reaching the desired exit point is difficult because it must be accomplished without direct visual feedback. Moreover, the stitching is a time consuming procedure repeated multiple times during suturing. Therefore, it would be desirable to enhance the surgical robots with the ability of performing automatic suturing. The focus of this work is on the automation of the stitching task. The thesis presents a model based path planning algorithm for the autonomous stitching. The method uses a nonlinear model for the curved needle - soft tissue interaction. The tissue is modeled as a deformable object using continuum mechanics tools. This thesis uses a mesh free deformable tissue model namely, Reproducing Kernel Particle Method (RKPM). RKPM was chosen as it has been proven to accurately handle large deformation and requires no re-meshing algorithms. This method has the potential to be more realistic in modeling various material characteristics by using appropriate strain energy functions. The stitching task is simulated using a constrained deformable model; the deformable tissue is constrained by the interaction with the curved needle. The stitching model was used for needle trajectory path planning during stitching. This new path planning algorithm for the robotic stitching was developed, implemented, and evaluated. Several simulations and experiments were conducted. The first group of simulations comprised random insertions from different insertion points without planning to assess the modeling method and the trajectory of the needle inside the tissue. Then the parameters of the simulations were set according to the measured experimental parameters. The proposed path planning method was tested using a surgical ETHICON needle of type SH 1=2 Circle with the radius of 8:88mm attached to a robotic manipulator. The needle was held by a grasper which is attached to the robotic arm. The experimental results illustrate that the path planned curved needle insertions are fifty percent more accurate than the unplanned ones. The results also show that this open loop approach is sensitive to model parameters.</p> / Master of Applied Science (MASc)

Formation planning and control for multi-agent robotic systems enables tasks to be completed more efficiently and robustly compared to using a single agent. Applications are found in fields such as agriculture, mining, autonomousvehicle platooning, surveillance, space exploration, etc. In this paper, a complete framework for formation path planning for holonomic ground robots in an obstacle-rich environment is proposed. The method utilizes the Fast Marching Square (FM2) path planning algorithm, and a formation keeping approach which falls within the Leader-Follower category. Contrary to most related works, the role of leader is dynamically assigned to avoid unnecessary rotation of the formation. Furthermore, the roles of the followers are also dynamically assigned to fit the current geometry of the formation. A flexible spring-damper system prevents inter-robot collisions and helps maintain the formation shape. An obstacle avoidance step at the end of the pipeline keeps the spring forces from driving robots into obstacles. The framework is tested on a formation consisting of three Unitree Go1 quadruped robots, both in the Gazebo simulation environment and in lab experiments. The results are successful and indicate that the method is feasible, although further work is needed to adjust the role assignment for larger formations, combine the framework with Simultaneous Localization and Mapping (SLAM) and provide a more robust handling of dynamic obstacles.

Motivated by experiments on titanium alloys of Petrinic et al. (2006), which show the formation of cracks through the growth and coalescence of voids in ductile fracture, we consider the problem of formulating a variational model in nonlinear elasticity compatible both with cavitation and the appearance of discontinuities across two-dimensional surfaces. As in the model for cavitation of Müller and Spector (1995) we address this problem, which is connected to the sequential weak continuity of the determinant of the deformation gradient in spaces of functions having low regularity, by means of adding an appropriate surface energy term to the elastic energy. Based upon considerations of invertibility, we derive an expression for the surface energy that admits a physical and a geometrical interpretation, and that allows for the formulation of a model with better analytical properties. We obtain, in particular, important regularity results for the inverses of deformations, as well as the weak continuity of the determinants and the existence of minimizers. We show, further, that the creation of surface can be modeled by carefully analyzing the jump set of the inverses, and we point out some connections between the analysis of cavitation and fracture, the theory of SBV functions, and the theory of Cartesian currents of Giaquinta, Modica, and Soucek. In addition to the above, we extend previous work of Sivaloganathan, Spector and Tilakraj (2006) on the approximation of minimizers for the problem of cavitation with a constraint in the number of flaw points, and present some numerical results for this problem.

Les Graphes sont des structures mathématiques puissantes constituant un outil de modélisation universel utilisé dans différents domaines de l'informatique, notamment dans le domaine de la reconnaissance de formes. L'appariement de graphes est l'opération principale dans le processus de la reconnaissance de formes à base de graphes. Dans ce contexte, trouver des solutions d'appariement de graphes, garantissant l'optimalité en termes de précision et de temps de calcul est un problème de recherche difficile et d'actualité. Dans cette thèse, nous nous intéressons à la résolution de ce problème dans deux domaines : la reconnaissance de formes 2D et 3D. Premièrement, nous considérons le problème d'appariement de graphes géométriques et ses applications sur la reconnaissance de formes 2D. Dance cette première partie, la reconnaissance des Kites (structures archéologiques) est l'application principale considérée. Nous proposons un "framework" complet basé sur les graphes pour la reconnaissance des Kites dans des images satellites. Dans ce contexte, nous proposons deux contributions. La première est la proposition d'un processus automatique d'extraction et de transformation de Kites a partir d'images réelles en graphes et un processus de génération aléatoire de graphes de Kites synthétiques. En utilisant ces deux processus, nous avons généré un benchmark de graphes de Kites (réels et synthétiques) structuré en 3 niveaux de bruit. La deuxième contribution de cette première partie, est la proposition d'un nouvel algorithme d'appariement pour les graphes géométriques et par conséquent pour les Kites. L'approche proposée combine les invariants de graphes au calcul de l'édition de distance géométrique. Deuxièmement, nous considérons le problème de reconnaissance des formes 3D ou nous nous intéressons à la reconnaissance d'objets déformables représentés par des graphes c.à.d. des tessellations de triangles. Nous proposons une décomposition des tessellations de triangles en un ensemble de sous structures que nous appelons triangle-étoiles. En se basant sur cette décomposition, nous proposons un nouvel algorithme d'appariement de graphes pour mesurer la distance entre les tessellations de triangles. L'algorithme proposé assure un nombre minimum de structures disjointes, offre une meilleure mesure de similarité en couvrant un voisinage plus large et utilise un ensemble de descripteurs qui sont invariants ou au moins tolérants aux déformations les plus courantes. Finalement, nous proposons une approche plus générale de l'appariement de graphes. Cette approche est fondée sur une nouvelle formalisation basée sur le problème de mariage stable. L'approche proposée est optimale en terme de temps d'exécution, c.à.d. la complexité est quadratique O(n2), et flexible en terme d'applicabilité (2D et 3D). Cette approche se base sur une décomposition en sous structures suivie par un appariement de ces structures en utilisant l'algorithme de mariage stable. L'analyse de la complexité des algorithmes proposés et l'ensemble des expérimentations menées sur les bases de graphes des Kites (réelle et synthétique) et d'autres bases de données standards (2D et 3D) attestent l'efficacité, la haute performance et la précision des approches proposées et montrent qu'elles sont extensibles et générales / Graphs are powerful mathematical modeling tools used in various fields of computer science, in particular, in Pattern Recognition. Graph matching is the main operation in Pattern Recognition using graph-based approach. Finding solutions to the problem of graph matching that ensure optimality in terms of accuracy and time complexity is a difficult research challenge and a topical issue. In this thesis, we investigate the resolution of this problem in two fields: 2D and 3D Pattern Recognition. Firstly, we address the problem of geometric graphs matching and its applications on 2D Pattern Recognition. Kite (archaeological structures) recognition in satellite images is the main application considered in this first part. We present a complete graph based framework for Kite recognition on satellite images. We propose mainly two contributions. The first one is an automatic process transforming Kites from real images into graphs and a process of generating randomly synthetic Kite graphs. This allowing to construct a benchmark of Kite graphs (real and synthetic) structured in different level of deformations. The second contribution in this part, is the proposition of a new graph similarity measure adapted to geometric graphs and consequently for Kite graphs. The proposed approach combines graph invariants with a geometric graph edit distance computation. Secondly, we address the problem of deformable 3D objects recognition, represented by graphs, i.e., triangular tessellations. We propose a new decomposition of triangular tessellations into a set of substructures that we call triangle-stars. Based on this new decomposition, we propose a new algorithm of graph matching to measure the distance between triangular tessellations. The proposed algorithm offers a better measure by assuring a minimum number of triangle-stars covering a larger neighbourhood, and uses a set of descriptors which are invariant or at least oblivious under most common deformations. Finally, we propose a more general graph matching approach founded on a new formalization based on the stable marriage problem. The proposed approach is optimal in term of execution time, i.e. the time complexity is quadratic O(n2) and flexible in term of applicability (2D and 3D). The analyze of the time complexity of the proposed algorithms and the extensive experiments conducted on Kite graph data sets (real and synthetic) and standard data sets (2D and 3D) attest the effectiveness, the high performance and accuracy of the proposed approaches and show that the proposed approaches are extensible and quite general

Les techniques d'insertion d'aiguille font partie des interventions chirurgicales les plus courantes. L'efficacité de ces interventions dépend fortement de la précision du positionnement des aiguilles dans un emplacement cible à l'intérieur du corps du patient. L'objectif principal dans cette thèse est de développer un système robotique autonome, capable d'insérer une aiguille flexible dans une structure déformable le long d'une trajectoire prédéfinie. L’originalité de ce travail se trouve dans l’utilisation de simulations inverses par éléments finis (EF) dans la boucle de contrôle du robot pour prédire la déformation des structures. La particularité de ce travail est que pendant l’insertion, les modèles EF sont continuellement recalés (étape corrective) grâce à l’information extraite d’un système d’imagerie peropératoire. Cette étape permet de contrôler l’erreur des modèles par rapport aux structures réelles et ainsi éviter qu'ils divergent. Une seconde étape (étape de prédiction) permet, à partir de la position corrigée, d’anticiper le comportement de structures déformables, en se reposant uniquement sur les prédictions des modèles biomécaniques. Ceci permet ainsi d’anticiper la commande du robot pour compenser les déplacements des tissus avant même le déplacement de l’aiguille. Expérimentalement, nous avions utilisé notre approche pour contrôler un robot réel afin d'insérer une aiguille flexible dans une mousse déformable le long d'une trajectoire (virtuelle) prédéfinie. Nous avons proposé une formulation basée sur des contraintes permettant le calcul d'étapes prédictives dans l'espace de contraintes offrant ainsi un temps d'insertion total compatible avec les applications cliniques. Nous avons également proposé un système de réalité augmentée pour la chirurgie du foie ouverte. La méthode est basée sur un recalage initial semi-automatique et un algorithme de suivi peropératoire basé sur des marqueurs (3D) optiques. Nous avons démontré l'applicabilité de cette approche en salle d'opération lors d'une chirurgie de résection hépatique. Les résultats obtenus au cours de ce travail de thèse ont conduit à trois publications (deux IROS et un ICRA) dans les conférences internationales puis à un journal (Transactions on Robotics) en cours de révision. / Needle-based interventions are among the least invasive surgical approaches to access deep internal structures into organs' volumes without damaging surrounding tissues. Unlike traditional open surgery, needle-based approaches only affect a localized area around the needle, reducing this way the occurrence of traumas and risks of complications \cite{Cowan2011}. Many surgical procedures rely on needles in nowadays clinical routines (biopsies, local anesthesia, blood sampling, prostate brachytherapy, vertebroplasty ...). Radiofrequency ablation (RFA) is an example of percutaneous procedure that uses heat at the tip of a needle to destroy cancer cells. Such alternative treatments may open new solutions for unrespectable tumors or metastasis (concerns about the age of the patient, the extent or localization of the disease). However, contrary to what one may think, needle-based approaches can be an exceedingly complex intervention. Indeed, the effectiveness of the treatment is highly dependent on the accuracy of the needle positioning (about a few millimeters) which can be particularly challenging when needles are manipulated from outside the patient with intra-operative images (X-ray, fluoroscopy or ultrasound ...) offering poor visibility of internal structures. Human factors, organs' deformations, needle deflection and intraoperative imaging modalities limitations can be causes of needle misplacement and rise significantly the technical level necessary to master these surgical acts. The use of surgical robots has revolutionized the way surgeons approach minimally invasive surgery. Robots have the potential to overcome several limitations coming from the human factor: for instance by filtering operator tremors, scaling the motion of the user or adding new degrees of freedom at the tip of instruments. A rapidly growing number of surgical robots has been developed and applied to a large panel of surgical applications \cite{Troccaz2012}. Yet, an important difficulty for needle-based procedures lies in the fact that both soft tissues and needles tend to deform as the insertion proceeds in a way that cannot be described with geometrical approaches. Standard solutions address the problem of the deformation extracting a set of features from per-operative images (also called \textit{visual servoing)} and locally adjust the pose/motion of the robot to compensate for deformations \cite{Hutchinson1996}. [...]To overcome these limitations, we introduce a numerical method allowing performing inverse Finite Element simulations in real-time. We show that it can be used to control an articulated robot while considering deformations of structures during needle insertion. Our approach relies on a forward FE simulation of a needle insertion (involving complex non-linear phenomena such as friction, puncture and needle constraints).[...]

O objetivo central deste trabalho é implementar modelos constitutivos hiperelásticos não lineares em um código computacional que faz análise não linear geométrica de cascas. São necessários, para este propósito, conceitos sobre álgebras linear e tensorial, cinemática, deformação, tensão, balanços, princípios variacionais, métodos numéricos e hiperelasticidade. Tal programa usa a formulação Lagrangiana posicional, o método dos elementos finitos, o princípio dos trabalhos virtuais e o método iterativo de Newton-Raphson para solução das equações não lineares. O elemento finito de casca possui dez nós, sete parâmetros por nó e variação linear da deformação ao longo da espessura. Para dedução dos novos modelos usou-se a decomposição multiplicativa do gradiente da função mudança de configuração, o tensor deformação de Green-Lagrange e o tensor da tensão de Piola-Kirchhoff de segunda espécie. O código desenvolvido foi usado em simulações de diversos exemplos e apresentou boa precisão na análise mecânica de polímeros naturais altamente deformáveis. A ocorrência do fenômeno travamento não se manifestou nas análises realizadas. A presente pesquisa confirmou outros trabalhos, reforçou a necessidade de se usar modelos hiperelásticos não lineares para simular o comportamento mecânico de polímeros naturais e apresentou resultados condizentes com dados experimentais existentes na literatura científica e às respectivas soluções analíticas. / The main objective of this work is to implement nonlinear hyperelastic constitutive models in a computational code of geometrically nonlinear analysis of shells. For this purpose, concepts of linear and tensor algebras, kinematics, strain, stress, balances, variational principles, numerical methods and hyperelasticity are necessary. Such program uses the positional Lagrangian formulation, the finite element method, the principle of virtual work and the iterative method of Newton-Raphson for the solution of the nonlinear equations. The shell finite element has ten nodes, seven parameters per node and presents linear variation of the strain along the thickness. To achieve the new constitutive models the multiplicative decomposition of the deformation gradient, the Green-Lagrange strain tensor and the second Piola-Kirchhoff stress tensor are used. The developed code is tested for simulations of various examples and presents good accuracy in the mechanical analysis of highly deformable natural rubber. The locking phenomena didn\'t appear in the proposed analysis. The present research confirms other works, corroborates the need of using nonlinear hyperelastic models to simulate the mechanical behavior of natural rubber and presents suitable results when compared to existent experimental data of the scientific literature and to the respective analytical solutions.