CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

3D Hand reconstruction from monocular camera with model-based priors

As virtual and augmented reality (VR/AR) technology gains popularity, facilitating intuitive digital interactions in 3D is of crucial importance. Tools such as VR controllers exist, but such devices support only a limited range of interactions, mapped onto complex sequences of button presses that can be intimidating to learn. In contrast, users already have an instinctive understanding of manual interactions in the real world, which is readily transferable to the virtual world. This makes hands the ideal mode of interaction for down-stream applications such as robotic teleoperation, sign-language translation, and computer-aided design. Existing hand-tracking systems come with several inconvenient limitations. Wearable solutions such as gloves and markers unnaturally limit the range of articulation. Multi-camera systems are not trivial to calibrate and have specialized hardware requirements which make them cumbersome to use. Given these drawbacks, recent research tends to focus on monocular inputs, as these do not constrain articulation and suitable devices are pervasive in everyday life. 3D reconstruction in this setting is severely under-constrained, however, due to occlusions and depth ambiguities. The majority of state-of-the-art works rely on a learning framework to resolve these ambiguities statistically; as a result they have several limitations in common. For example, they require a vast amount of annotated 3D data that is labor intensive to obtain and prone to systematic error. Additionally, traits that are hard to quantify with annotations - the details of individual hand appearance - are difficult to reconstruct in such a framework. Existing methods also make the simplifying assumption that only a single hand is present in the scene. Two-hand interactions introduce additional challenges, however, in the form of inter-hand occlusion, left-right confusion, and collision constraints, that single hand methods cannot address. To tackle the aforementioned shortcomings of previous methods, this thesis advances the state-of-the-art through the novel use of model-based priors to incorporate hand-specific knowledge. In particular, this thesis presents a training method that reduces the amount of annotations required and is robust to systemic biases; it presents the first tracking method that addresses the challenging two-hand-interaction scenario using monocular RGB video, and also the first probabilistic method to model image ambiguity for two-hand interactions. Additionally, this thesis also contributes the first parametric hand texture model with example applications in hand personalization.Virtual- und Augmented-Reality-Technologien (VR/AR) gewinnen rapide an Beliebtheit und Einfluss, und so ist die Erleichterung intuitiver digitaler Interaktionen in 3D von wachsender Bedeutung. Zwar gibt es Tools wie VR-Controller, doch solche Geräte unterstützen nur ein begrenztes Spektrum an Interaktionen, oftmals abgebildet auf komplexe Sequenzen von Tastendrücken, deren Erlernen einschüchternd sein kann. Im Gegensatz dazu haben Nutzer bereits ein instinktives Verständnis für manuelle Interaktionen in der realen Welt, das sich leicht auf die virtuelle Welt übertragen lässt. Dies macht Hände zum idealen Werkzeug der Interaktion für nachgelagerte Anwendungen wie robotergestützte Teleoperation, Übersetzung von Gebärdensprache und computergestütztes Design. Existierende Hand-Tracking Systeme leiden unter mehreren unbequemen Einschränkungen. Tragbare Lösungen wie Handschuhe und aufgesetzte Marker schränken den Bewegungsspielraum auf unnatürliche Weise ein. Systeme mit mehreren Kameras erfordern genaue Kalibrierung und haben spezielle Hardwareanforderungen, die ihre Anwendung umständlich gestalten. Angesichts dieser Nachteile konzentriert sich die neuere Forschung tendenziell auf monokularen Input, da so Bewegungsabläufe nicht gestört werden und geeignete Geräte im Alltag allgegenwärtig sind. Die 3D-Rekonstruktion in diesem Kontext stößt jedoch aufgrund von Okklusionen und Tiefenmehrdeutigkeiten schnell an ihre Grenzen. Die Mehrheit der Arbeiten auf dem neuesten Stand der Technik setzt hierbei auf ein ML-Framework, um diese Mehrdeutigkeiten statistisch aufzulösen; infolgedessen haben all diese mehrere Einschränkungen gemein. Beispielsweise benötigen sie eine große Menge annotierter 3D-Daten, deren Beschaffung arbeitsintensiv und anfällig für systematische Fehler ist. Darüber hinaus sind Merkmale, die mit Anmerkungen nur schwer zu quantifizieren sind – die Details des individuellen Erscheinungsbildes – in einem solchen Rahmen schwer zu rekonstruieren. Bestehende Verfahren gehen auch vereinfachend davon aus, dass nur eine einzige Hand in der Szene vorhanden ist. Zweihand-Interaktionen bringen jedoch zusätzliche Herausforderungen in Form von Okklusion der Hände untereinander, Links-Rechts-Verwirrung und Kollisionsbeschränkungen mit sich, die Einhand-Methoden nicht bewältigen können. Um die oben genannten Mängel früherer Methoden anzugehen, bringt diese Arbeit den Stand der Technik durch die neuartige Verwendung modellbasierter Priors voran, um Hand-spezifisches Wissen zu integrieren. Insbesondere stellt diese Arbeit eine Trainingsmethode vor, die die Menge der erforderlichen Annotationen reduziert und robust gegenüber systemischen Verzerrungen ist; es wird die erste Tracking-Methode vorgestellt, die das herausfordernde Zweihand-Interaktionsszenario mit monokularem RGB-Video angeht, und auch die erste probabilistische Methode zur Modellierung der Bildmehrdeutigkeit für Zweihand-Interaktionen. Darüber hinaus trägt diese Arbeit auch das erste parametrische Handtexturmodell mit Beispielanwendungen in der Hand-Personalisierung bei

Atlas construction and image analysis using statistical cardiac models

International audienceThis paper presents a brief overview of current trends in the construction of population and multi-modal heart atlases in our group and their application to atlas-based cardiac image analysis. The technical challenges around the construction of these atlases are organized around two main axes: groupwise image registration of anatomical, motion and fiber images and construction of statistical shape models. Application-wise, this paper focuses on the extraction of atlas-based biomarkers for the detection of local shape or motion abnormalities, addressing several cardiac applications where the extracted information is used to study and grade different pathologies. The paper is concluded with a discussion about the role of statistical atlases in the integration of multiple information sources and the potential this can bring to in-silico simulations

Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings

Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras. In unconstrained real-world settings, however, such methods are surpassed by recent appearance-based methods due to difficulties in modeling factors such as illumination changes and other visual artifacts. We present a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods. Despite having been trained exclusively on synthetic data, our method exceeds the state of the art for iris localization and eye shape registration on real-world imagery. We then use the detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods. Our approach outperforms existing model-fitting and appearance-based methods in the context of person-independent and personalized gaze estimation

Personalized Multi-Scale Modeling of the Atria: Heterogeneities, Fiber Architecture, Hemodialysis and Ablation Therapy

This book targets three fields of computational multi-scale cardiac modeling. First, advanced models of the cellular atrial electrophysiology and fiber orientation are introduced. Second, novel methods to create patient-specific models of the atria are described. Third, applications of personalized models in basic research and clinical practice are presented. The results mark an important step towards the patient-specific model-based atrial fibrillation diagnosis, understanding and treatment

Three-dimensional cardiac computational modelling: methods, features and applications

[EN] The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.This work was partially supported by the "VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica" from the Ministerio de Economia y Competitividad of Spain (TIN2012-37546-C03-01 and TIN2011-28067) and the European Commission (European Regional Development Funds - ERDF - FEDER) and by "eTorso project" (GVA/2013-001404) from the Generalitat Valenciana (Spain). ALP is financially supported by the program "Ayudas para contratos predoctorales para la formacion de doctores" from the Ministerio de Economia y Competitividad of Spain (BES-2013-064089).López Pérez, AD.; Sebastián Aguilar, R.; Ferrero De Loma-Osorio, JM. (2015). 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