To support the replication of ?Human Object Interaction Detection via Multi-level Conditioned Network", which was presented at ICMR'20, this companion paper provides the details of the artifacts. Human Object Interaction Detection (HOID) aims to recognize fine-grained object-specific human actions, which demands the capabilities of both visual perception and reasoning. In this paper, we explain the file structure of the source code and publish the details of our experiments settings. We also provide a program for component analysis to assist other researchers with experiments on alternative models that are not included in our experiments. Moreover, we provide a demo program for facilitating the use of our model.

Abstract Endometriosis is a common gynecologic condition typically treated via laparoscopic surgery. Its visual versatility makes it hard to identify for non-specialized physicians and challenging to classify or localize via computer-aided analysis. In this work, we take a first step in the direction of localized endometriosis recognition in laparoscopic gynecology videos using region-based deep neural networks Faster R-CNN and Mask R-CNN. We in particular use and further develop publicly available data for transfer learning deep detection models according to distinctive visual lesion characteristics. Subsequently, we evaluate the performance impact of different data augmentation techniques, including selected geometrical and visual transformations, specular reflection removal as well as region tracking across video frames. Finally, particular attention is given to creating reasonable data segmentation for training, validation and testing. The best performing result surprisingly is achieved by randomly applying simple cropping combined with rotation, resulting in a mean average segmentation precision of 32.4% at 50-95% intersection over union overlap (64.2% for 50% overlap).

Endometriosis is a common women's condition exhibiting a manifold visual appearance in various body-internal locations. Having such properties makes its identification very difficult and error-prone, at least for laymen and non-specialized medical practitioners. In an attempt to provide assistance to gynecologic physicians treating endometriosis, this demo paper describes a system that is trained to segment one frequently occurring visual appearance of endometriosis, namely dark endometrial implants. The system is capable of analyzing laparoscopic surgery videos, annotating identified implant regions with multi-colored overlays and displaying a detection summary for improved video browsing.

Despite the fact that automatic content analysis has made remarkable progress over the last decade - mainly due to significant advances in machine learning - interactive video retrieval is still a very challenging problem, with an increasing relevance in practical applications. The Video Browser Showdown (VBS) is an annual evaluation competition that pushes the limits of interactive video retrieval with state-of-the-art tools, tasks, data, and evaluation metrics. In this paper, we analyse the results and outcome of the 8th iteration of the VBS in detail. We first give an overview of the novel and considerably larger V3C1 dataset and the tasks that were performed during VBS 2019. We then go on to describe the search systems of the six international teams in terms of features and performance. And finally, we perform an in-depth analysis of the per-team success ratio and relate this to the search strategies that were applied, the most popular features, and problems that were experienced. A large part of this analysis was conducted based on logs that were collected during the competition itself. This analysis gives further insights into the typical search behavior and differences between expert and novice users. Our evaluation shows that textual search and content browsing are the most important aspects in terms of logged user interactions. Furthermore, we observe a trend towards deep learning based features, especially in the form of labels generated by artificial neural networks. But nevertheless, for some tasks, very specific content-based search features are still being used. We expect these findings to contribute to future improvements of interactive video search systems.

Accumulating recordings of daily conducted surgical interventions such as endoscopic procedures for the long term generates very large video archives that are both difficult to search and explore. Since physicians utilize this kind of media routinely for documentation, treatment planning or education and training, it can be considered a crucial task to make said archives manageable in regards to discovering or retrieving relevant content. We present an interactive tool including a multitude of modalities for browsing, searching and filtering medical content, demonstrating its usefulness on over 140 hours of pre-processed laparoscopic surgery videos.

Computers are powerful tools capable of solving a great variety of ever so complex problems, yet training them to interpret even the simplest video scenes can prove more challenging than one might imagine. Still being one of the major problems in computer vision, this issue recently is addressed by utilizing promising deep learning approaches in order to recognize objects and their semantics. For achieving this goal, huge artificial networks are fed with many human-created annotations using more or less sophisticated tools for speeding up the otherwise time-consuming task of manual annotation. Purposefully refraining from designing yet another of these annotation tools, in this work we strive for evaluating what makes existing ones great or not, i.e. we aim at determining effectiveness and efficiency of state-of-the-art object annotation tools when employed for annotating different kinds of video content. Our findings in a user study evaluating three comparable tools on three videos of distinct domains indicate a significant difference in annotation effort from a video perspective, yet no significance regarding utilized tools. Further, we determine a significant correlation between annotation time and accuracy.

demonstration Share on Alternative inputs for games and AR/VR applications: deep headbanging on the web Authors: Philipp Moll Alpen-Adria-Universität Klagenfurt Alpen-Adria-Universität KlagenfurtView Profile , Andreas Leibetseder Alpen-Adria-Universität Klagenfurt Alpen-Adria-Universität KlagenfurtView Profile , Sabrina Kletz Alpen-Adria-Universität Klagenfurt Alpen-Adria-Universität KlagenfurtView Profile , Mathias Lux Alpen-Adria-Universität Klagenfurt Alpen-Adria-Universität KlagenfurtView Profile , Bernd Muenzer Alpen-Adria-Universität Klagenfurt Alpen-Adria-Universität KlagenfurtView Profile Authors Info & Claims MMSys '19: Proceedings of the 10th ACM Multimedia Systems ConferenceJune 2019 Pages 320–323https://doi.org/10.1145/3304109.3323832Published:18 June 2019Publication History 2citation167DownloadsMetricsTotal Citations2Total Downloads167Last 12 Months20Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Modern day endoscopic technology enables medical staff to conveniently document surgeries via recording raw treatment footage, which can be utilized for planning further proceedings, future case revisitations or even educational purposes. However, the prospect of manually perusing recorded media files constitutes a tedious additional workload on physicians' already packed timetables and therefore ultimately represents a burden rather than a benefit. The aim of this PhD project is to improve upon this situation by closely collaborating with medical experts in order to devise datasets and systems to facilitate semi-automatic post-surgical media processing.

Modern imaging technology enables medical practitioners to perform minimally invasive surgery (MIS), i.e. a variety of medical interventions inflicting minimal trauma upon patients, hence, greatly improving their recoveries. Not only patients but also surgeons can benefit from this technology, as recorded media can be utilized for speeding-up tedious and time-consuming tasks such as treatment planning or case documentation. In order to improve the predominantly manually conducted process of analyzing said media, with this work we publish four datasets extracted from gynecologic, laparoscopic interventions with the intend on encouraging research in the field of post-surgical automatic media analysis. These datasets are designed with the following use cases in mind: medical image retrieval based on a query image, detection of instrument counts, surgical actions and anatomical structures, as well as distinguishing on which anatomical structure a certain action is performed. Furthermore, we provide suggestions for evaluation metrics and first baseline experiments.

When regarding physicians' tremendously packed timetables, it comes as no surprise that they start managing even critical situations hastily in order to cope with the high demands laid out for them. Apart from treating patients' conditions they as well are required to perform time-consuming administrative tasks, including post-surgery video analyses. Concerning documentation of minimally invasive surgeries (MIS), specifically endoscopy, such processes usually involve repeatedly perusing through lengthy, in the worst case uncut recordings - a redundant task that nowadays can be optimized by using readily available technology: we present a tool for annotating endoscopic video frames targeting a specific use case - endometriosis, i.e. the dislocation of uterine-like tissue.

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.