Time-varying volume visualization

Volume rendering is a very active research field in Computer Graphics because of its wide range of applications in various sciences, from medicine to flow mechanics. In this report, we survey a state-of-the-art on time-varying volume rendering. We state several basic concepts and then we establish several criteria to classify the studied works: IVR versus DVR, 4D versus 3D+time, compression techniques, involved architectures, use of parallelism and image-space versus object-space coherence. We also address other related problems as transfer functions and 2D cross-sections computation of time-varying volume data. All the papers reviewed are classified into several tables based on the mentioned classification and, finally, several conclusions are presented.Preprin

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Modelling human pose and shape based on a database of human 3D scans

Generating realistic human shapes and motion is an important task both in the motion picture industry and in computer games. In feature films, high quality and believability are the most important characteristics. Additionally, when creating virtual doubles the generated charactes have to match as closely as possible to given real persons. In contrast, in computer games the level of realism does not need to be as high but real-time performance is essential. It is desirable to meet all these requirements with a general model of human pose and shape. In addition, many markerless human tracking methods applied, e.g., in biomedicine or sports science can benefit greatly from the availability of such a model because most methods require a 3D model of the tracked subject as input, which can be generated on-the-fly given a suitable shape and pose model. In this thesis, a comprehensive procedure is presented to generate different general models of human pose. A database of 3D scans spanning the space of human pose and shape variations is introduced. Then, four different approaches for transforming the database into a general model of human pose and shape are presented, which improve the current state of the art. Experiments are performed to evaluate and compare the proposed models on real-world problems, i.e., characters are generated given semantic constraints and the underlying shape and pose of humans given 3D scans, multi-view video, or uncalibrated monocular images is estimated.Die Erzeugung realistischer Menschenmodelle ist eine wichtige Anwendung in der Filmindustrie und bei Computerspielen. In Spielen ist Echtzeitsynthese unabdingbar aber der Detailgrad muß nicht so hoch sein wie in Filmen. Für virtuelle Doubles, wie sie z.B. in Filmen eingesetzt werden, muss der generierte Charakter dem gegebenen realen Menschen möglichst ähnlich sein. Mit einem generellen Modell für menschliche Pose und Körperform ist es möglich alle diese Anforderungen zu erfüllen. Zusätzlich können viele Verfahren zur markerlosen Bewegungserfassung, wie sie z.B. in der Biomedizin oder in den Sportwissenschaften eingesetzt werden, von einem generellen Modell für Pose und Körperform profitieren. Da diese ein 3D Modell der erfassten Person benötigen, das jetzt zur Laufzeit generiert werden kann. In dieser Doktorarbeit wird ein umfassender Ansatz vorgestellt, um verschiedene Modelle für Pose und Körperform zu berechnen. Zunächst wird eine Datenbank von 3D Scans aufgebaut, die Pose- und Körperformvariationen von Menschen umfasst. Dann werden vier verschiedene Verfahren eingeführt, die daraus generelle Modelle für Pose und Körperform berechnen und Probleme beim Stand der Technik beheben. Die vorgestellten Modelle werden auf realistischen Problemstellungen getestet. So werden Menschenmodelle aus einigen wenigen Randbedingungen erzeugt und Pose und Körperform von Probanden wird aus 3D Scans, Multi-Kamera Videodaten und Einzelbildern der bekleideten Personen geschätzt