Stefan Kahl

Teaching the Information Retrieval Process Using a Web-Based Environment and Game Mechanics

Predicting the performance of individual components of information retrieval systems, in particular the complex interactions between those components, is still challenging. Therefore, professionals are needed for the implementation and configuration of retrieval systems and retrieval components. Our web-based application, called Xtrieval Web Lab, enables newcomers and learners to gain practical knowledge about the information retrieval process. They can arrange a multitude of components of retrieval systems and evaluate them with real world data without utilizing a programming language. Game mechanics guide the learners in their discovery process and motivate them.

Aufbereitung augenmedizinischer Bild-, Patienten- und Diagnosedaten zum Zwecke der Forschung - Ethikrichtlinen und deren praktische Umsetzung.

In der vorliegenden Arbeit stellen die Authoren einen technischen Workflow vor, der darstellt wie in der Praxis gesetzliche Vorgaben im Bezug auf ethische Fragestellungen umgesetzt werden können. Dabei wird auf die rechtlichen Grundlagen auf Bundesebene eingegangen, aber auch auf die Besonderheiten auf Länderebene und der lokalen Umsetzung. Am Fallbeispiel der Kooperation zwischen der Juniorprofessur Media Computing an der TU Chemnitz und der Augenklinik des Klinikum Chemnitz gGmbH zeigen wir dabei welche Vorgaben seitens des Ethikbeauftragten einzuhalten waren hinsichtlich der Anonymisierung von Patientendaten, der Verschlüsselung, dem Transport/Transfer von der Klinik an die Universität, Speicherung und Zugriffsrechte der Daten. Eingegangen wird insbesondere auf unterschiedliche Aspekte in der retrospektiven Forschung mit Patientendaten. Damit soll insbesondere Einsteigern auf dem Gebiet der Forschung mit klinischen Daten ein erster Einblick ermöglicht werden.

Acoustic Event Classification Using Convolutional Neural Networks

The classification of human-made acoustic events is important for the monitoring and recognition of human activities or critical behavior. In our experiments on acoustic event classification for the utilization in the sector of health care, we defined different acoustic events which represent critical events for elderly or people with disabilities in ambient assisted living environments or patients in hospitals. This contribution presents our work for acoustic event classification using deep learning techniques. We implemented and trained various convolutional neural networks for the extraction of deep feature vectors making use of current best practices in neural network design to establish a baseline for acoustic event classification. We convert chunks of audio signals into magnitude spectrograms and treat acoustic events as images. Our data set contains 20 different acoustic events which were collected in two different recording sessions combining human and environmental sounds. Our results demonstrate how efficient convolutional neural networks perform in the domain of acoustic event classification.

Processing Digitized (S)VHS Archives: An Approach of Content-based Retargeting for local TV Stations

We propose an adaptive system, which automates the workflow of combining low resolution (S)VHS archive material and todays high resolution footage in one video, to assist professional video editors. During this process, old footage of an aspect ratio of 4:3 needs to be cropped in order to use the Full-HD 16:9 screen space, whilst the overall aesthetics and content of the source video should be preserved. If done manually, this is an extensive and time consuming task. The challenge is to automatically adjust the framing according to the depicted content. Our approach aims at an automated detection of the most interesting regions of the source footage for adaptive cropping based on various visual features of representative key frames.