Capturing Hands in Action using Discriminative Salient Points and Physics Simulation

Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.Comment: Accepted for publication by the International Journal of Computer Vision (IJCV) on 16.02.2016 (submitted on 17.10.14). A combination into a single framework of an ECCV'12 multicamera-RGB and a monocular-RGBD GCPR'14 hand tracking paper with several extensions, additional experiments and detail

Biological hydrogels as selective diffusion barriers

The controlled exchange of molecules between organelles, cells, or organisms and their environment is crucial for life. Biological gels such as mucus, the extracellular matrix (ECM), and the biopolymer barrier within the nuclear pore are well suited to achieve such a selective exchange, allowing passage of particular molecules while rejecting many others. Although hydrogel-based filters are integral parts of biology, clear concepts of how their barrier function is controlled at a microscopic level are still missing. We summarize here our current understanding of how selective filtering is established by different biopolymer-based hydrogels. We ask if the modulation of microscopic particle transport in biological hydrogels is based on a generic filtering principle which employs biochemical/biophysical interactions with the filtered molecules rather than size-exclusion effects.National Institutes of Health (U.S.) (Grant P50GM068763)MIT Start-up FundsGerman Academic Exchange Service (Postdoctoral Fellowship

4-D single particle tracking of synthetic and proteinaceous microspheres reveals preferential movement of nuclear particles along chromatin – poor tracks

BACKGROUND: The dynamics of nuclear organization, nuclear bodies and RNPs in particular has been the focus of many studies. To understand their function, knowledge of their spatial nuclear position and temporal translocation is essential. Typically, such studies generate a wealth of data that require novel methods in image analysis and computational tools to quantitatively track particle movement on the background of moving cells and shape changing nuclei. RESULTS: We developed a novel 4-D image processing platform (TIKAL) for the work with laser scanning and wide field microscopes. TIKAL provides a registration software for correcting global movements and local deformations of cells as well as 2-D and 3-D tracking software. With this new tool, we studied the dynamics of two different types of nuclear particles, namely nuclear bodies made from GFP-NLS-vimentin and microinjected 0.1 μm – wide polystyrene beads, by live cell time-lapse microscopy combined with single particle tracking and mobility analysis. We now provide a tool for the automatic 3-D analysis of particle movement in parallel with the acquisition of chromatin density data. CONCLUSIONS: Kinetic analysis revealed 4 modes of movement: confined obstructed, normal diffusion and directed motion. Particle tracking on the background of stained chromatin revealed that particle movement is directly related to local reorganization of chromatin. Further a direct comparison of particle movement in the nucleoplasm and the cytoplasm exhibited an entirely different kinetic behaviour of vimentin particles in both compartments. The kinetics of nuclear particles were slightly affected by depletion of ATP and significantly disturbed by disruption of actin and microtubule networks. Moreover, the hydration state of the nucleus had a strong impact on the mobility of nuclear bodies since both normal diffusion and directed motion were entirely abolished when cells were challenged with 0.6 M sorbitol. This effect correlated with the compaction of chromatin. We conclude that alteration in chromatin density directly influences the mobility of protein assemblies within the nucleus

Articulated Object Tracking from Visual Sensory Data for Robotic Manipulation

Roboti juhtimine liigestatud objekti manipuleerimisel vajab robustset ja täpsetobjekti oleku hindamist. Oleku hindamise tulemust kasutatakse tagasisidena vastavate roboti liigutuste arvutamisel soovitud manipulatsiooni tulemuse saavutamiseks. Selles töös uuritakse robootilise manipuleerimise visuaalse tagasiside teostamist. Tehisnägemisele põhinevat servode liigutamist juhitakse ruutplaneerimise raamistikus võimaldamaks humanoidsel robotil läbi viia objekti manipulatsiooni. Esitletakse tehisnägemisel põhinevat liigestatud objekti oleku hindamise meetodit. Me näitame väljapakutud meetodi efektiivsust mitmel erineval eksperimendil HRP-4 humanoidse robotiga. Teeme ka ettepaneku ühendada masinõppe ja serva tuvastamise tehnikad liigestatud objekti manipuleerimise markeerimata visuaalse tagasiside teostamiseks reaalajas.In order for a robot to manipulate an articulated object, it needs to know itsstate (i.e. its pose); that is to say: where and in which configuration it is. Theresult of the object’s state estimation is to be provided as a feedback to the control to compute appropriate robot motion and achieve the desired manipulation outcome. This is the main topic of this thesis, where articulated object state estimation is solved using visual feedback. Vision based servoing is implemented in a Quadratic Programming task space control framework to enable humanoid robot to perform articulated objects manipulation. We thoroughly developed our methodology for vision based articulated object state estimation on these bases.We demonstrate its efficiency by assessing it on several real experiments involving the HRP-4 humanoid robot. We also propose to combine machine learning and edge extraction techniques to achieve markerless, realtime and robust visual feedback for articulated object manipulation

Physics-Based Probabilistic Motion Compensation of Elastically Deformable Objects

A predictive tracking approach and a novel method for visual motion compensation are introduced, which accurately reconstruct and compensate the deformation of the elastic object, even in the case of complete measurement information loss. The core of the methods involves a probabilistic physical model of the object, from which all other mathematical models are systematically derived. Due to flexible adaptation of the models, the balance between their complexity and their accuracy is achieved

Hybrid Architectures for Object Pose and Velocity Tracking at the Intersection of Kalman Filtering and Machine Learning

The study of object perception algorithms is fundamental for the development of robotic platforms capable of planning and executing actions involving objects with high precision, reliability and safety. Indeed, this topic has been vastly explored in both the robotic and computer vision research communities using diverse techniques, ranging from classical Bayesian filtering to more modern Machine Learning techniques, and complementary sensing modalities such as vision and touch. Recently, the ever-growing availability of tools for synthetic data generation has substantially increased the adoption of Deep Learning for both 2D tasks, as object detection and segmentation, and 6D tasks, such as object pose estimation and tracking. The proposed methods exhibit interesting performance on computer vision benchmarks and robotic tasks, e.g. using object pose estimation for grasp planning purposes. Nonetheless, they generally do not consider useful information connected with the physics of the object motion and the peculiarities and requirements of robotic systems. Examples are the necessity to provide well-behaved output signals for robot motion control, the possibility to integrate modelling priors on the motion of the object and algorithmic priors. These help exploit the temporal correlation of the object poses, handle the pose uncertainties and mitigate the effect of outliers. Most of these concepts are considered in classical approaches, e.g. from the Bayesian and Kalman filtering literature, which however are not as powerful as Deep Learning in handling visual data. As a consequence, the development of hybrid architectures that combine the best features from both worlds is particularly appealing in a robotic setting. Motivated by these considerations, in this Thesis, I aimed at devising hybrid architectures for object perception, focusing on the task of object pose and velocity tracking. The proposed architectures use Kalman filtering supported by state-of-the-art Deep Neural Networks to track the 6D pose and velocity of objects from images. The devised solutions exhibit state-of-the-art performance, increased modularity and do not require training to implement the actual tracking behaviors. Furthermore, they can track even fast object motions despite the possible non-negligible inference times of the adopted neural networks. Also, by relying on data-driven Kalman filtering, I explored a paradigm that enables to track the state of systems that cannot be easily modeled analytically. Specifically, I used this approach to learn the measurement model of soft 3D tactile sensors and address the problem of tracking the sliding motion of hand-held objects

Capturing Hand-Object Interaction and Reconstruction of Manipulated Objects

Hand motion capture with an RGB-D sensor gained recently a lot of research attention, however, even most recent approaches focus on the case of a single isolated hand. We focus instead on hands that interact with other hands or with a rigid or articulated object. Our framework successfully captures motion in such scenarios by combining a generative model with discriminatively trained salient points, collision detection and physics simulation to achieve a low tracking error with physically plausible poses. All components are unified in a single objective function that can be optimized with standard optimization techniques. We initially assume a-priori knowledge of the object’s shape and skeleton. In case of unknown object shape there are existing 3d reconstruction methods that capitalize on distinctive geometric or texture features. These methods though fail for textureless and highly symmetric objects like household articles, mechanical parts or toys. We show that extracting 3d hand motion for in-hand scanning e↵ectively facilitates the reconstruction of such objects and we fuse the rich additional information of hands into a 3d reconstruction pipeline. Finally, although shape reconstruction is enough for rigid objects, there is a lack of tools that build rigged models of articulated objects that deform realistically using RGB-D data. We propose a method that creates a fully rigged model consisting of a watertight mesh, embedded skeleton and skinning weights by employing a combination of deformable mesh tracking, motion segmentation based on spectral clustering and skeletonization based on mean curvature flow

Computer vision methods for unconstrained gesture recognition in the context of sign language annotation

Cette thèse porte sur l'étude des méthodes de vision par ordinateur pour la reconnaissance de gestes naturels dans le contexte de l'annotation de la Langue des Signes. La langue des signes (LS) est une langue gestuelle développée par les sourds pour communiquer. Un énoncé en LS consiste en une séquence de signes réalisés par les mains, accompagnés d'expressions du visage et de mouvements du haut du corps, permettant de transmettre des informations en parallèles dans le discours. Même si les signes sont définis dans des dictionnaires, on trouve une très grande variabilité liée au contexte lors de leur réalisation. De plus, les signes sont souvent séparés par des mouvements de co-articulation. Cette extrême variabilité et l'effet de co-articulation représentent un problème important dans les recherches en traitement automatique de la LS. Il est donc nécessaire d'avoir de nombreuses vidéos annotées en LS, si l'on veut étudier cette langue et utiliser des méthodes d'apprentissage automatique. Les annotations de vidéo en LS sont réalisées manuellement par des linguistes ou experts en LS, ce qui est source d'erreur, non reproductible et extrêmement chronophage. De plus, la qualité des annotations dépend des connaissances en LS de l'annotateur. L'association de l'expertise de l'annotateur aux traitements automatiques facilite cette tâche et représente un gain de temps et de robustesse. Le but de nos recherches est d'étudier des méthodes de traitement d'images afin d'assister l'annotation des corpus vidéo: suivi des composantes corporelles, segmentation des mains, segmentation temporelle, reconnaissance de gloses. Au cours de cette thèse nous avons étudié un ensemble de méthodes permettant de réaliser l'annotation en glose. Dans un premier temps, nous cherchons à détecter les limites de début et fin de signe. Cette méthode d'annotation nécessite plusieurs traitements de bas niveau afin de segmenter les signes et d'extraire les caractéristiques de mouvement et de forme de la main. D'abord nous proposons une méthode de suivi des composantes corporelles robuste aux occultations basée sur le filtrage particulaire. Ensuite, un algorithme de segmentation des mains est développé afin d'extraire la région des mains même quand elles se trouvent devant le visage. Puis, les caractéristiques de mouvement sont utilisées pour réaliser une première segmentation temporelle des signes qui est par la suite améliorée grâce à l'utilisation de caractéristiques de forme. En effet celles-ci permettent de supprimer les limites de segmentation détectées en milieu des signes. Une fois les signes segmentés, on procède à l'extraction de caractéristiques visuelles pour leur reconnaissance en termes de gloses à l'aide de modèles phonologiques. Nous avons évalué nos algorithmes à l'aide de corpus internationaux, afin de montrer leur avantages et limitations. L'évaluation montre la robustesse de nos méthodes par rapport à la dynamique et le grand nombre d'occultations entre les différents membres. L'annotation résultante est indépendante de l'annotateur et représente un gain de robustese important.This PhD thesis concerns the study of computer vision methods for the automatic recognition of unconstrained gestures in the context of sign language annotation. Sign Language (SL) is a visual-gestural language developed by deaf communities. Continuous SL consists on a sequence of signs performed one after another involving manual and non-manual features conveying simultaneous information. Even though standard signs are defined in dictionaries, we find a huge variability caused by the context-dependency of signs. In addition signs are often linked by movement epenthesis which consists on the meaningless gesture between signs. The huge variability and the co-articulation effect represent a challenging problem during automatic SL processing. It is necessary to have numerous annotated video corpus in order to train statistical machine translators and study this language. Generally the annotation of SL video corpus is manually performed by linguists or computer scientists experienced in SL. However manual annotation is error-prone, unreproducible and time consuming. In addition de quality of the results depends on the SL annotators knowledge. Associating annotator knowledge to image processing techniques facilitates the annotation task increasing robustness and speeding up the required time. The goal of this research concerns on the study and development of image processing technique in order to assist the annotation of SL video corpus: body tracking, hand segmentation, temporal segmentation, gloss recognition. Along this PhD thesis we address the problem of gloss annotation of SL video corpus. First of all we intend to detect the limits corresponding to the beginning and end of a sign. This annotation method requires several low level approaches for performing temporal segmentation and for extracting motion and hand shape features. First we propose a particle filter based approach for robustly tracking hand and face robust to occlusions. Then a segmentation method for extracting hand when it is in front of the face has been developed. Motion is used for segmenting signs and later hand shape is used to improve the results. Indeed hand shape allows to delete limits detected in the middle of a sign. Once signs have been segmented we proceed to the gloss recognition using lexical description of signs. We have evaluated our algorithms using international corpus, in order to show their advantages and limitations. The evaluation has shown the robustness of the proposed methods with respect to high dynamics and numerous occlusions between body parts. Resulting annotation is independent on the annotator and represents a gain on annotation consistency

Toward a morphodynamic model of the cell: Signal processing for cell modeling

From a systems biology perspective, the cell is the principal element of information integration. Therefore, understanding the cell in its spatiotemporal context is the key to unraveling many of the still unknown mechanisms of life and disease. This article reviews image processing aspects relevant to the quantification of cell morphology and dynamics. We cover both acquisition (hardware) and analysis (software) related issues, in a multiscale fashion, from the detection of cellular components to the description of the entire cell in relation to its extracellular environment. We then describe ongoing efforts to integrate all this vast and diverse information along with data about the biomechanics of the cell to create a credible model of cell morphology and behavior.Carlos Ortiz-de-Solorzano and Arrate Muñoz-Barrutia were supported by the Spanish Ministry of Economy and Competitiveness grants with reference DPI2012-38090-C03-02 and TEC2013-48552-C02, respectively. Michal Kozubek was supported by the Czech Science Foundation (302/12/G157)