Andreas Leibetseder

A low-cost NDN testbed on banana pi routers

The computer communication research community has a significant interest in the paradigm of ICN. Continuously, new proposals for ICN-related challenges (caching, forwarding, etc.) are published. However, due to a lack of a readily available testbed, the majority of these proposals are evaluated by either theoretical analysis and/or conducting network simulations, potentially masking further challenges that are not observable in synthetic environments. Therefore, this article presents a framework for an ICN testbed using low-budget physical hardware with little deployment and maintenance effort for the individual researcher; specifically, named data networking is considered. The employed hardware and software are powerful enough for most research projects, but extremely resource-intensive tasks may push both components toward their limits. The testbed framework is based on well established open source software and provides the tools to readily investigate important ICN characteristics on physical hardware emulating arbitrary network topologies. The article discusses the testbed architecture and provides first results obtained from emulations that investigate the performance of various forwarding strategies. The results indicate that further challenges have to be overcome when heading towards a real-world deployment of ICN-based communication.

The ITEC Collaborative Video Search System at the Video Browser Showdown 2018

We present our video search system for the Video Browser Showdown (VBS) 2018 competition. It is based on the collaborative system used in 2017, which already performed well but also revealed high potential for improvement. Hence, based on our experience we introduce several major improvements, particularly (1) a strong optimization of similarity search, (2) various improvements for concept-based search, (3) a new flexible video inspector view, and (4) extended collaboration features, as well as numerous minor adjustments and enhancements, mainly concerning the user interface and means of user interaction. Moreover, we present a spectator view that visualizes the current activity of the team members to the audience to make the competition more attractive.

Real-Time Image-based Smoke Detection in Endoscopic Videos

The nature of endoscopy as a type of minimally invasive surgery (MIS) requires surgeons to perform complex operations by merely inspecting a live camera feed. Inherently, a successful intervention depends upon ensuring proper working conditions, such as skillful camera handling, adequate lighting and removal of confounding factors, such as fluids or smoke. The latter is an undesirable byproduct of cauterizing tissue and not only constitutes a health hazard for the medical staff as well as the treated patients, it can also considerably obstruct the operating physicians field of view. Therefore, as a standard procedure the gaseous matter is evacuated by using specialized smoke suction systems that typically are activated manually whenever considered appropriate. We argue that image-based smoke detection can be employed to undertake such a decision, while as well being a useful indicator for relevant scenes in post-procedure analyses. This work represents a continued effort to previously conducted studies utilizing pre-trained convolutional neural networks (CNNs) and threshold-based saturation analysis. Specifically, we explore further methodologies for comparison and provide as well as evaluate a public dataset comprising over 100K smoke/non-smoke images extracted from the Cholec80 dataset, which is composed of 80 different cholecystectomy procedures. Having applied deep learning to merely 20K images of a custom dataset, we achieve Receiver Operating Characteristic (ROC) curves enclosing areas of over 0.98 for custom datasets and over 0.77 for the public dataset. Surprisingly, a fixed threshold for saturation-based histogram analysis still yields areas of over 0.78 and 0.75.

Lapgyn4: a dataset for 4 automatic content analysis problems in the domain of laparoscopic gynecology

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.

lifeXplore at the Lifelog Search Challenge 2018

With the growing hype for wearable devices recording biometric data comes the readiness to capture and combine even more personal information as a form of digital diary - lifelogging today is practiced ever more and can be categorized anywhere between an informative hobby and a life-changing experience. From an information processing point of view, analyzing the entirety of such multi-source data is immensely challenging, which is why the first Lifelog Search Challenge 2018 competition is brought into being, as to encourage the development of efficient interactive data retrieval systems. Answering this call, we present a retrieval system based on our video search system diveXplore, which has successfully been used in the Video Browser Showdown 2017 and 2018. Due to the different task definition and available data corpus, the base system was adapted and extended to this new challenge. The resulting lifeXplore system is a flexible retrieval and exploration tool that offers various easy-to-use, yet still powerful search and browsing features that have been optimized for lifelog data and for usage by novice users. Besides efficient presentation and summarization of lifelog data, it includes searchable feature maps, concept and metadata filters, similarity search and sketch search.

Image-Based Smoke Detection in Laparoscopic Videos

The development and improper removal of smoke during minimally invasive surgery (MIS) can considerably impede a patient’s treatment, while additionally entailing serious deleterious health effects. Hence, state-of-the-art surgical procedures employ smoke evacuation systems, which often still are activated manually by the medical staff or less commonly operate automatically utilizing industrial, highly-specialized and operating room (OR) approved sensors. As an alternate approach, video analysis can be used to take on said detection process – a topic not yet much researched in aforementioned context. In order to advance in this sector, we propose utilizing an image-based smoke classification task on a pre-trained convolutional neural network (CNN). We provide a custom data set of over 30 000 laparoscopic smoke/non-smoke images, part of which served as training data for GoogLeNet-based [41] CNN models. To be able to compare our research for evaluation, we separately developed a non-CNN classifier based on observing the saturation channel of a sample picture in the HSV color space. While the deep learning approaches yield excellent results with Receiver Operating Characteristic (ROC) curves enclosing areas of over 0.98, the computationally much less costly analysis of an image’s saturation histogram under certain circumstances can, surprisingly, as well be a good indicator for smoke with areas under the curves (AUCs) of around 0.92–0.97.

Extracting and Using Medical Expert Knowledge to Advance in Video Processing for Gynecologic Endoscopy

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.

Evaluation of Visual Content Descriptors for Supporting Ad-Hoc Video Search Tasks at the Video Browser Showdown

Since 2017 the Video Browser Showdown (VBS) collaborates with TRECVID and interactively evaluates Ad-Hoc Video Search (AVS) tasks, in addition to Known-Item Search (KIS) tasks. In this video search competition the participants have to find relevant target scenes to a given textual query within a specific time limit, in a large dataset consisting of 600 h of video content. Since usually the number of relevant scenes for such an AVS query is rather high, the teams at the VBS 2017 could find only a small portion of them. One way to support them at the interactive search would be to automatically retrieve other similar instances of an already found target scene. However, it is unclear which content descriptors should be used for such an automatic video content search, using a query-by-example approach. Therefore, in this paper we investigate several different visual content descriptors (CNN Features, CEDD, COMO, HOG, Feature Signatures and HOF) for the purpose of similarity search in the TRECVID IACC.3 dataset, used for the VBS. Our evaluation shows that there is no single descriptor that works best for every AVS query, however, when considering the total performance over all 30 AVS tasks of TRECVID 2016, CNN features provide the best performance.

Sketch-Based Similarity Search for Collaborative Feature Maps

Past editions of the annual Video Browser Showdown (VBS) event have brought forward many tools targeting a diverse amount of techniques for interactive video search, among which sketch-based search showed promising results. Aiming at exploring this direction further, we present a custom approach for tackling the problem of finding similarities in the TRECVID IACC.3 dataset via hand-drawn pictures using color compositions together with contour matching. The proposed methodology is integrated into the established Collaborative Feature Maps (CFM) system, which has first been utilized in the VBS 2017 challenge.

Automatic Smoke Classification in Endoscopic Video

Medical smoke evacuation systems enable proper, filtered removal of toxic fumes during surgery, while stabilizing internal pressure during endoscopic interventions. Typically activated manually, they, however, are prone to inefficient utilization: tardy activation enables smoke to interfere with ongoing surgeries and late deactivation wastes precious resources. In order to address such issues, in this work we demonstrate a vision-based tool indicating endoscopic smoke – a first step towards automatic activation of said systems and avoiding human misconduct. In the back-end we employ a pre-trained convolutional neural network (CNN) model for distinguishing images containing smoke from others.