Progressive Shape Models

International audienceIn this paper we address the problem of recovering both the topology and the geometry of a deformable shape using temporal mesh sequences. The interest arises in multi-camera applications when unknown natural dynamic scenes are captured. While several approaches allow recovery of shape models from static scenes, few consider dynamic scenes with evolving topology and without prior knowledge. In this nonetheless generic situation, a single time observation is not necessarily enough to infer the correct topology of the observed shape and evidences must be accumulated over time in order to learn this topology and to enable temporally consistent modelling. This appears to be a new problem for which no formal solution exists. We propose a principled approach based on the assumption that the observed objects have a fixed topology. Under this assumption, we can progressively learn the topology meanwhile capturing the deformation of the dynamic scene. The approach has been successfully experimented on several standard 4D datasets and we believe that it paves the way to more general multi-view scene capture and analysis.Dans cet article nous nous concentrons sur un problème récurrent des systèmes d'acquisition 4D : l'apprentissage de la géométrie et de la topologie d'une scène déformable à partir d'une séquence temporelle de maillages. Il s'agit d'une étape fondamentale dans le traitement de scènes naturelles et dynamiques. Tandis que de nombreux travaux ont été menés pour la reconstruction de scènes statiques, assez peu considèrent le cas de scènes dynamiques dont la topologie évolue et sans connaissances \apriori. Dans cette situation, une simple observation à un unique instant de temps n'est souvent pas suffisante pour retrouver entièrement l'information de topologie propre à la scène observée. Il semble ainsi évident que les indices sur la forme doivent être accumulés intelligemment sur une séquence complète afin d'acquerir une information aussi complète que possible sur la topologie de la scène et permettre l'apprentissage d'un modèle cohérent à la fois spatialement et temporellement. A notre connaissance cela semble un problème nouveau pour lequel aucune solution formelle n'a été proposée. Nous formulons dans cette thèse un principe de solution basé sur l'hypothèse que les objets composant la scène observée possèdent une topologie fixe. A partir de cette hypothèse de base nous pouvons progressivement apprendre la topologie et en parallèle capturer les déformations d'une scène dynamique. Les travaux présentés dans cette partie visent à retrouver une information de basse fréquence sur la géométrie de la scène. En l'état actuel, la méthode que nous proposons ne peut pas être directement utilisée pour accumuler les informations de bas niveau (détails de la surface) sur une séquence de maillages

Computer- and robot-assisted Medical Intervention

Medical robotics includes assistive devices used by the physician in order to make his/her diagnostic or therapeutic practices easier and more efficient. This chapter focuses on such systems. It introduces the general field of Computer-Assisted Medical Interventions, its aims, its different components and describes the place of robots in that context. The evolutions in terms of general design and control paradigms in the development of medical robots are presented and issues specific to that application domain are discussed. A view of existing systems, on-going developments and future trends is given. A case-study is detailed. Other types of robotic help in the medical environment (such as for assisting a handicapped person, for rehabilitation of a patient or for replacement of some damaged/suppressed limbs or organs) are out of the scope of this chapter.Comment: Handbook of Automation, Shimon Nof (Ed.) (2009) 000-00

On Mean Pose and Variability of 3D Deformable Models

International audienceWe present a novel methodology for the analysis of complex object shapes in motion observed by multiple video cameras. In particular, we propose to learn local surface rigidity probabilities (i.e., deformations), and to estimate a mean pose over a temporal sequence. Local deformations can be used for rigidity-based dynamic surface segmentation, while a mean pose can be used as a sequence keyframe or a cluster prototype and has therefore numerous applications, such as motion synthesis or sequential alignment for compression or morphing. We take advantage of recent advances in surface tracking techniques to formulate a generative model of 3D temporal sequences using a probabilistic framework, which conditions shape fitting over all frames to a simple set of intrinsic surface rigidity properties. Surface tracking and rigidity variable estimation can then be formulated as an Expectation-Maximization inference problem and solved by alternatively minimizing two nested fixed point iterations. We show that this framework provides a new fundamental building block for various applications of shape analysis, and achieves comparable tracking performance to state of the art surface tracking techniques on real datasets, even compared to approaches using strong kinematic priors such as rigid skeletons

CAD, CAE and rapid prototyping methods applied in long bones orthopaedics

U radu su prikazane metode za analizu ljudskih koštanih zglobova. Prvo, upotrebom CT slika, definisani su 'čvrsti' delovi kao glavne komponente kosti i 'meki' delovi kao što su ligamenti ili meniskusi. Ove komponente uvoze se u modul za montažu parametrizovanog okruženja i dobija se biomehanički model ljudskog hoda, koji se izvozi u kinematsko simulaciono okruženje i koristi za analizu konačnim elementima, gde se prvo definišu kinematski parametri. Sa ovako definisanim parametrima može se izvršiti zamena kinematskih i dinamičkih simulacija podsistema klasičnim, normalnim kretanjem. Nakon interpretacije rezultata, mogu se modifikovati početni parametri biomehaničkih podsistema. U sledećoj fazi, komponente podsistema su podeljene sukcesivno i dobijena je struktura konačnih elemenata za ceo biomehanički sistem spojeva koji učestvuju u ljudskoj lokomociji.The paper presents some methods used to analyze human bone joints. First, there were defined the 'hard' parts as the main bone components and 'soft' parts as ligaments or menisci using CT images. These components are imported into a parameterized environment assembly module and a biomechanical model of human walking is being obtained, which is exported to a kinematic simulation environment and finite element analysis, where first the kinematic parameters are defined. With these defined parameters, the kinematic and dynamic simulation of the subsystems for classical, normal motion can be switched. Following the interpretation of the results, the initial parameters of the biomechanical subsystems may be modified. In the next phase, the components of the subsystems are divided successively and the finite element structure is obtained for the entire biomechanical system of the joints that participate in human locomotion

CAD, CAE and rapid prototyping methods applied in long bones orthopaedics

U radu su prikazane metode za analizu ljudskih koštanih zglobova. Prvo, upotrebom CT slika, definisani su 'čvrsti' delovi kao glavne komponente kosti i 'meki' delovi kao što su ligamenti ili meniskusi. Ove komponente uvoze se u modul za montažu parametrizovanog okruženja i dobija se biomehanički model ljudskog hoda, koji se izvozi u kinematsko simulaciono okruženje i koristi za analizu konačnim elementima, gde se prvo definišu kinematski parametri. Sa ovako definisanim parametrima može se izvršiti zamena kinematskih i dinamičkih simulacija podsistema klasičnim, normalnim kretanjem. Nakon interpretacije rezultata, mogu se modifikovati početni parametri biomehaničkih podsistema. U sledećoj fazi, komponente podsistema su podeljene sukcesivno i dobijena je struktura konačnih elemenata za ceo biomehanički sistem spojeva koji učestvuju u ljudskoj lokomociji.The paper presents some methods used to analyze human bone joints. First, there were defined the 'hard' parts as the main bone components and 'soft' parts as ligaments or menisci using CT images. These components are imported into a parameterized environment assembly module and a biomechanical model of human walking is being obtained, which is exported to a kinematic simulation environment and finite element analysis, where first the kinematic parameters are defined. With these defined parameters, the kinematic and dynamic simulation of the subsystems for classical, normal motion can be switched. Following the interpretation of the results, the initial parameters of the biomechanical subsystems may be modified. In the next phase, the components of the subsystems are divided successively and the finite element structure is obtained for the entire biomechanical system of the joints that participate in human locomotion

Internal constraints and arrested relaxation in main-chain nematic elastomers

Abstract: Nematic liquid crystal elastomers (N-LCE) exhibit intriguing mechanical properties, such as reversible actuation and soft elasticity, which manifests as a wide plateau of low nearly-constant stress upon stretching. N-LCE also have a characteristically slow stress relaxation, which sometimes prevents their shape recovery. To understand how the inherent nematic order retards and arrests the equilibration, here we examine hysteretic stress-strain characteristics in a series of specifically designed main-chain N-LCE, investigating both macroscopic mechanical properties and the microscopic nematic director distribution under applied strains. The hysteretic features are attributed to the dynamics of thermodynamically unfavoured hairpins, the sharp folds on anisotropic polymer strands, the creation and transition of which are restricted by the nematic order. These findings provide a new avenue for tuning the hysteretic nature of N-LCE at both macro- and microscopic levels via different designs of polymer networks, toward materials with highly nonlinear mechanical properties and shape-memory applications

Méthodes mathématiques d’analyse d’image pour les études de population transversales et longitudinales

In medicine, large scale population analysis aim to obtain statistical information in order to understand better diseases, identify their risk factors, develop preventive and curative treatments and improve the quality of life of the patients.In this thesis, we first introduce the medical context of Alzheimer’s disease, recall some concepts of statistical learning and the challenges that typically occurwhen applied in medical imaging. The second part focus on cross-sectional studies,i.e. at a single time point. We present an efficient method to classify white matter lesions based on support vector machines. Then we discuss the use of manifoldlearning techniques for image and shape analysis. Finally, we present extensions ofLaplacian eigenmaps to improve the low-dimension representations of patients usingthe combination of imaging and clinical data. The third part focus on longitudinalstudies, i.e. between several time points. We quantify the hippocampus deformations of patients via the large deformation diffeomorphic metric mapping frameworkto build disease progression classifiers. We introduce novel strategies and spatialregularizations for the classification and identification of biomarkers.En médecine, les analyses de population à grande échelle ont pour but d’obtenir des informations statistiques pour mieux comprendre des maladies, identifier leurs facteurs de risque, développer des traitements préventifs et curatifs et améliorer la qualité de vie des patients.Dans cette thèse, nous présentons d’abord le contexte médical de la maladie d’Alzheimer, rappelons certains concepts d’apprentissage statistique et difficultés rencontrées lors de l’application en imagerie médicale. Dans la deuxième partie,nous nous intéressons aux analyses transversales, c-a-d ayant un seul point temporel.Nous présentons une méthode efficace basée sur les séparateurs à vaste marge (SVM)permettant de classifier des lésions dans la matière blanche. Ensuite, nous étudions les techniques d’apprentissage de variétés pour l’analyse de formes et d’images, et présentons deux extensions des Laplacian eigenmaps améliorant la représentation de patients en faible dimension grâce à la combinaison de données d’imagerie et cliniques. Dans la troisième partie, nous nous intéressons aux analyses longitudinales, c-a-d entre plusieurs points temporels. Nous quantifions les déformations des hippocampus de patients via le modèle des larges déformations par difféomorphismes pour classifier les évolutions de la maladie. Nous introduisons de nouvelles stratégies et des régularisations spatiales pour la classification et l’identification de marqueurs biologiques

An Implicit Parametric Morphable Dental Model

3D Morphable models of the human body capture variations among subjects and are useful in reconstruction and editing applications. Current dental models use an explicit mesh scene representation and model only the teeth, ignoring the gum. In this work, we present the first parametric 3D morphable dental model for both teeth and gum. Our model uses an implicit scene representation and is learned from rigidly aligned scans. It is based on a component-wise representation for each tooth and the gum, together with a learnable latent code for each of such components. It also learns a template shape thus enabling several applications such as segmentation, interpolation, and tooth replacement. Our reconstruction quality is on par with the most advanced global implicit representations while enabling novel applications. Project page: https://vcai.mpi-inf.mpg.de/projects/DMM