Abstract: Recent research efforts have reported findings on user Quality of Experience (QoE) of immersive virtual reality (VR) experiences. Truly immersive multimedia experiences also include multisensory components such as factional, tactile etc., in addition to audiovisual stimuli. In this context, this paper reports the results of a user QoE study of an olfaction-enhanced immersive VR environment. The results presented compare the user QoE between two groups (VR vs VR + Olfaction) and consider how the addition of olfaction affected user QoE levels (considering sense of enjoyment, immersion and discomfort). Self-reported measures via post-test questionnaire (10 questions) only revealed one statistically significant difference between the groups; in terms of how users felt with respect to their senses being stimulated. The presence of olfaction in the VR environment did not have a statistically significant effect in terms of user levels of enjoyment, immersion and discomfort.

Abstract: Given a large-scale video archive of surgical interventions and a medical image showing a specific moment of an operation, how to find the most image-related videos efficiently without the utilization of additional semantic characteristics? In this paper, we investigate a novel content-based approach of linking medical images with relevant video segments arising from endoscopic procedures. We propose to approximate the video segments' content-based features by gradient-based signatures and to index these signatures with the Minkowski distance in order to determine the most query-like video segments efficiently. We benchmark our approach on a large endoscopic image and video archive and show that our approach achieves a significant improvement in efficiency in comparison to the state-of-the-art while maintaining high accuracy.

Abstract: Bowel preparation (cleansing) is considered to be a key precondition for successful colonoscopy (endoscopic examination of the bowel). The degree of bowel cleansing directly affects the possibility to detect diseases and may influence decisions on screening and follow-up examination intervals. An accurate assessment of bowel preparation quality is therefore important. Despite the use of reliable and validated bowel preparation scales, the grading may vary from one doctor to another. An objective and automated assessment of bowel cleansing would contribute to reduce such inequalities and optimize use of medical resources. This would also be a valuable feature for automatic endoscopy reporting in the future. In this paper, we present Nerthus, a dataset containing videos from inside the gastrointestinal (GI) tract, showing different degrees of bowel cleansing. By providing this dataset, we invite multimedia researchers to contribute in the medical field by making systems automatically evaluate the quality of bowel cleansing for colonoscopy. Such innovations would probably contribute to improve the medical field of GI endoscopy.

Abstract: We propose an image processing algorithm to automatically separate compound figures appearing in scientific articles. We classify compound images into two classes and apply different algorithms for detecting vertical and horizontal separators to each class: the edge-based algorithm aims at detecting visible edges between subfigures, whereas the band-based algorithm tries to detect whitespace separating subfigures (separator bands). The proposed algorithm has been evaluated on two datasets for compound figure separation (CFS) in the biomedical domain and compares well to semi-automatic or more comprehensive state-of-the-art approaches. Additional experiments investigate CFS effectiveness and classification accuracy of various classifier implementations.

Abstract: Fitness- or exergames are ubiquitously available, but often lack the main ingredient of successfully gamified systems: fun. This can be attributed to the typical way of designing such games -- highly focusing on specific physical activities, thus, gamifying fitness. Instead, we propose a novel alternate approach to improve motivation for exergaming, which we call fitnessification: integrating physical exercise into very popular games that have been developed keeping fun in mind and frequently are played for long periods of time -- so-called AAA games. In order to evaluate this concept, we have conducted a comparative study examining voluntary participants' reactions to testing an ergometer controlled casual game as well as a modified AAA game. Results indicate strong tendencies of players preferring the newly introduced AAA approach over the casual fitness game.

Abstract: In this report we describe our approach to the fully automatic Ad-hoc video search task for TRECVID 2016. We describe how we obtain training data from the web, create according CNN models for the provided queries and use them to classify keyframes from a custom sub-shot detection method. The resulting classifications are fed into a Lucene index in order to obtain the shots that match the query. We also discuss our results and point out potentials for further improvements.

Abstract: Today, the global share and increase of Internet traffic is largely caused by multimedia delivery, mainly encompassing video, audio and image sharing on social, news, and entertainment platforms. This fact is well known to the Internet research community, which tries to counteract by increasing the content delivery efficiency. So-called Information-Centric Networks (ICN) are of considerable interest, advertised as enablers for intelligent networks, where effective delivery is to be provided as an inherent network feature. Most research proposals in this area are evaluated in simulated environments, using simulation frameworks such as OMNeT++ or ns-3. However, simulations always have shortcomings and cannot substitute measurements in physical networks. In this demonstration, we show how to readily set up an ICN-based testbed using low-budget single-board computers to conduct comprehensive emulations. We choose the scenario of pull-based adaptive video delivery as a showcase and evaluate the performance of different client-based adaptation mechanisms at the application level and different content forwarding strategies at the network level. All of the presented tools and visualization features are provided as open source contributions to the community.

Abstract: Content-Centric Networking (CCN) research addresses the mismatch between the modern usage of the Internet and its outdated architecture. Importantly, CCN routers use various caching strategies to locally cache content frequently requested by end users. However, it is unclear which content shall be stored and when it should be replaced. In this work, we employ novel techniques towards intelligent administration of CCN routers. Our approach allows for autonomous switching between existing strategies in response to changing content request patterns using rule-based stream reasoning framework LARS which extends Answer Set Programming for streams. The obtained possibility for flexible router configuration at runtime allows for faster experimentation and may result in significant performance gains, as shown in our evaluation.

Abstract: We propose a novel video browsing approach that aims at optimally integrating traditional, machine-based retrieval methods with an interface design optimized for human browsing performance. Advanced video retrieval and filtering (e.g., via color and motion signatures, and visual concepts) on a desktop is combined with a storyboard-based interface design on a tablet optimized for quick, brute-force visual inspection. Both modules run independently but exchange information to significantly minimize the data for visual inspection and compensate mistakes made by the search algorithms.

Abstract: Small drones are being utilized in monitoring, delivery of goods, public safety, and disaster management among other civil applications. Due to their sizes, capabilities, payload limitations, and limited flight time, it is not far-fetched to expect multiple networked and coordinated drones incorporated into the air traffic. In this paper, we describe a high-level architecture for the design of a collaborative aerial system that consists of drones with on-board sensors and embedded processing, sensing, coordination, and communication&networking capabilities. We present a multi-drone system consisting of quadrotors and demonstrate its potential in a disaster assistance scenario. Furthermore, we illustrate the challenges in the design of drone networks and present potential solutions based on the lessons we have learned so far.

Abstract: This paper proposes and evaluates a modular architecture of an autonomous unmanned aerial vehicle (UAV) system for search and rescue missions. Multiple multicopters are coordinated using a distributed control system. The system is implemented in the Robot Operating System (ROS) and is capable of providing a real-time video stream from a UAV to one or more base stations using a wireless communications infrastructure. The system supports a heterogeneous set of UAVs and camera sensors. If necessary, an operator can interfere and reduce the autonomy. The system has been tested in an outdoor mission serving as a proof of concept. Some insights from these tests are described in the paper.