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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.
Das Erkennen von Personen auf Fotos und in Videos geschieht in aktuellen Klassifizierungsverfahren durch die Zuordnung eines Köperteils zu jedem Pixel. Anschließend werden die gefundenen Körperteile zu Skeletten zusammengefasst. Im Falle mehrerer abgebildeter Personen ergibt sich das Problem der Zuordnung der Körperteile zu den verschiedenen Skeletten. Es wurde in dieser Arbeit ein auf dem Suurballe-Algorithmus basierendes graphentheoretisches Verfahren entwickelt, das diese Aufgabe löst. Aufbauend auf im Wesentlichen abstandsabhängigen Kantengewichten wird eine k-kürzeste-disjunkte-Wege-Suche durchgeführt. Unter Einbezug von möglicherweise fehlenden Körperteilen durch Unsichtbar-Knoten und automatisches Aussortieren zu viel gefundener Körperteile werden so die k-optimalen Skelette gefunden. Die Methode wurde an idealisierten computergenerierten Bildern mit einer Trefferquote von 100 % gefundener Personen getestet. Ein Test mit realen Bilddaten lieferte eine Trefferquote von 71,7 %.
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 %.
Background and objective: Accurate 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 methods: FOI 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.
Results: In 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.
Conclusions: With 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.
To improve the feasibility of endoscopic inspection processes we developed a system that provides online information about position, orientation and viewing direction of endoscopes, to support the analysis of endoscopic images and to ease the operational handling of the equipment. The setup is based on an industrial endoscope consisting of a camera, various MEMS and multimodal data fusion. The software contains algorithms for feature and geometric structure recognition as well as Kalman filters. To track the distal end of the endoscope and to generate 3D point cloud data in real time the optical and photometrical characteristics of the system are registered and the movement of the endoscope is reconstructed by using image processing techniques.
Structured light has become a widespread technique for the development of camera-based 3D sensors. The structured illumination provides texture to homogeneous objects and thus allows for the reliable determination of the disparity of each object point in a stereo-camera setting. Even a monocular 3D sensor is possible if the light projector has a fixed relative position to the camera and if the structured light is coded, i.e. the position within the whole light pattern can be reconstructed uniquely from a small local window of the pattern, the uniqueness window. Coded patterns with such a uniqueness property are called Perfect SubMaps (PSM). In our paper we focus on the design and evaluation of the subset of symmetric isolated binary toroidal PSMs (SIBTPSM) for structured light patterns, because of their beneficial properties with respect to the signal-to-noise ratio and the use with laser light sources and DOEs. We define several figures of merit that are relevant for the practical use of PSMs in a 3D sensor: the PSM size, the size of the uniqueness window, the Hamming distance, the density, and the homogeneity. We have created SIBTPSMs using our own dedicated algorithms and have designed and fabricated DOEs that produce these patterns with large fan angles of 61° × 47° when used with near-infrared diode lasers (λ = 830nm). We analyze the influence of these characteristics on the 3D measurement process by theory, simulations, and experiments. The patterns of publicly available DOEs based on SIBTPSMs are used for comparison and reference. Our results show that the PSM width, the uniqueness window size, the minimum and average Hamming distances, and the uniformity have strong impact on either speed or quality of the 3D reconstruction, whereas the point density and the PSM height are of minor importance.