A Modular Mobile Device for Real-Time 3D Streaming

In recent years, 3D movies and streaming films have become increasingly popular. Even mobile devices, such as mobile phones and tablet computers, are becoming more popular, more powerful and have better multimedia capabilities. Nevertheless, compact mobile devices to broadcast live 3D videos in real time are barely available. In this article, we provide a modular and mobile solution that allows 3D video streaming in real time at 25 frames per second and with a resolution of 1280 × 720 pixels (720p). As operating system, we use standard hardware components combined with Android. Furthermore, we will describe the restraints of the development, and how they were solved.In den letzten Jahren sind 3D-Filme und das Streamen von Filmen immer populärer geworden. Auch mobile Endgeräte, wie Smartphones oder Tablet Computer, nehmen an Verbreitung zu, werden leistungsstärker und verfügen über verbesserte Multimediafähigkeiten. Trotzdem gibt es kaum kompakte mobile Geräte, um live 3D-Videos in Echtzeit zu übertragen. In diesem Artikel stellen wir eine modulare und mobile Lösung vor, die es ermöglicht, 3D-Videos in Echtzeit, bei 25 Bildern pro Sekunde und mit einer Auflösung von 1280 × 720 Pixeln (720p) zu senden. Dabei verwenden wir Standard-Hardware-Komponenten und nutzen Android als Betriebssystem. Des Weiteren beschreiben wir, welche Schwierigkeiten bei der Entwicklung auftraten und wie diese gelöst wurden

Image recognition of multi-perspective data for intelligent analysis of gestures and actions

The BERMUDA project started in January 2015 and was successfully completed after less than three years in August 2017. A technical set-up and an image processing and analysis software were developed to record and evaluate multi-perspective videos. Based on two cameras, positioned relatively far from one another with tilted axes, synchronized videos were recorded in the laboratory and in real life. The evaluation comprised the background elimination, the body part classification, the clustering, the assignment to persons and eventually the reconstruction of the skeletons. Based on the skeletons, machine learning techniques were developed to recognize the poses of the persons and next for the actions performed. It was, for example, possible to detect the action of a punch, which is relevant in security issues, with a precision of 51.3 % and a recall of 60.6 %.Das Projekt BERMUDA konnte im Januar 2015 begonnen und nach knapp drei Jahren im August 2017 erfolgreich abgeschlossen werden. Es wurden ein technischer Aufbau und eine Bildverarbeitungs- und Analysesoftware entwickelt, mit denen sich multiperspektivische Videos aufzeichnen und auswerten lassen. Basierend auf zwei in größerem Abstand gewinkelt positionierten Kameras wurden synchrone Videos sowohl im Labor als auch in realen Umgebungen aufgenommen. Die Auswertung umfasst die Hintergrundeliminierung, die Körperteilklassifikation, ein Clustering, die Zuordnung zu Personen und schließlich die Rekonstruktion der Skelette. Ausgehend von den Skeletten wurden Methoden des maschinellen Lernens zur Erkennung der Haltungen und darauf aufbauend zur Gestenerkennung entwickelt. Beispielhaft konnte die im Sicherheitskontext relevante Handlung des Schlagens mit einer Genauigkeit von 51,3 % und einer Trefferquote von 60,6 % erkannt werden

Fluorescence optical imaging feature selection with machine learning for differential diagnosis of selected rheumatic diseases

Background and objectiveAccurate and fast diagnosis of rheumatic diseases affecting the hands is essential for further treatment decisions. Fluorescence optical imaging (FOI) visualizes inflammation-induced impaired microcirculation by increasing signal intensity, resulting in different image features. This analysis aimed to find specific image features in FOI that might be important for accurately diagnosing different rheumatic diseases.Patients and methodsFOI images of the hands of patients with different types of rheumatic diseases, such as rheumatoid arthritis (RA), osteoarthritis (OA), and connective tissue diseases (CTD), were assessed in a reading of 20 different image features in three phases of the contrast agent dynamics, yielding 60 different features for each patient. The readings were analyzed for mutual differential diagnosis of the three diseases (One-vs-One) and each disease in all data (One-vs-Rest). In the first step, statistical tools and machine-learning-based methods were applied to reveal the importance rankings of the features, that is, to find features that contribute most to the model-based classification. In the second step machine learning with a stepwise increasing number of features was applied, sequentially adding at each step the most crucial remaining feature to extract a minimized subset that yields the highest diagnostic accuracy.ResultsIn total, n = 605 FOI of both hands were analyzed (n = 235 with RA, n = 229 with OA, and n = 141 with CTD). All classification problems showed maximum accuracy with a reduced set of image features. For RA-vs.-OA, five features were needed for high accuracy. For RA-vs.-CTD ten, OA-vs.-CTD sixteen, RA-vs.-Rest five, OA-vs.-Rest eleven, and CTD-vs-Rest fifteen, features were needed, respectively. For all problems, the final importance ranking of the features with respect to the contrast agent dynamics was determined.ConclusionsWith the presented investigations, the set of features in FOI examinations relevant to the differential diagnosis of the selected rheumatic diseases could be remarkably reduced, providing helpful information for the physician

Design and quality metrics of point patterns for coded structured light illumination with diffractive optical elements in optical 3D sensors

Der Abstract ist unter http://dx.doi.org/10.1117/12.2270248 zu finden.The abstract can be found on http://dx.doi.org/10.1117/12.2270248