The Distance Transform and its Computation - An Introduction -
Strutz, Tilo (2021)
Technical paper, June, 2021, TECH/2021/06, arxiv.org/abs/2106.03503v1.
DOI: 10.48550/arXiv.2106.03503
Distance transformation is an image processing technique used for many different
applications. Related to a binary image, the general idea is to determine the distance of
all background points to the nearest object point (or vice versa). In this tutorial, different
approaches are explained in detail and compared using examples. Corresponding source
code is provided to facilitate own investigations. A particular objective of this tutorial
is to clarify the difference between arbitrary distance transforms and exact Euclidean
distance transformations.
Traveling Santa Problem: Optimization of a Million-Households Tour Within One Hour
Strutz, Tilo (2021)
Frontiers in Robotics and AI, 8:652417, 8.
DOI: 10.3389/frobt.2021.652417
Open Access Peer Reviewed
Finding the shortest tour visiting all given points at least ones belongs to the most famous optimization problems until today (TSP . . . travelling salesman problem). Optimal solutions exist for many problems up to several ten thousand points. The major difficulty in solving larger problems is the required computational complexity. This shifts the research from finding the optimum with no time limitation to approaches that find good but sub-optimal solutions in pre-defined limited time. This paper proposes a new approach for two-dimensional symmetric problems with more than a million coordinates that is able to create good initial tours within few minutes. It is based on a hierarchical clustering strategy and supports parallel processing. In addition, a method is proposed that can correct unfavourable paths with moderate computational complexity. The new approach is superior to state-of-the-art methods when applied to TSP instances with non-uniformly distributed coordinates.
Spatial Resolution-Independent CNN-based Person Detection in Agricultural Image Data
Strutz, Tilo; Leipnitz, Alexander; Jokisch, Oliver (2020)
5th Int. Conf. on Interactive Collaborative Robotics, ICR.
Peer Reviewed
Advanced object detectors based on Convolutional Neural Networks (CNNs) offer high detection rates for many application scenarios but only within their respective training, validation and test data. Recent studies show that such methods provide a limited generalization ability for unknown data, even for small image modifications including a limited scale invariance. Reliable person detection with aerial robots (Unmanned Aerial Vehicles, UAVs) is an essential task to fulfill high security requirements or to support robot control, communication, and human-robot interaction. Particularly in an agricultural context persons need to be detected from a long distance and a high altitude to allow the UAV an adequate and timely response. While UAVs are able to produce high resolution images that enable the detection of persons from a longer distance, typical CNN input layer sizes are comparably low. The inevitable scaling of images to match the input-layer size can lead to a further reduction in person sizes. We investigate the reliability of different YOLOv3 architectures for person detection in regard to those input-scaling effects. The popular VisDrone data set with its varying image resolutions and relatively small depiction of humans is used as well as high resolution UAV images from an agricultural data set. To overcome the scaling problem, an algorithm is presented for segmenting high resolution images in overlapping tiles that match the input-layer size. The number and overlap of the tiles are dynamically determined based on the image resolution. It is shown that the detection rate of very small persons in high resolution images can be improved using this tiling approach.
Towards a Practical Virtual Office for Mobile Knowledge Workers
Ofek, E.; Grubert, Jens; Pahud, M.; Phillips, Mark ; Kristensson, P. O. (2020)
arXiv preprint arXiv:2009.02947.
Open Access
Totally Different and yet so Alike: Three Concepts to Use Scrum in Higher Education
Klopp, M.; Gold-Veerkamp, C.; Abke, J.; Borgeest, K.; Reuter, R.; Jahn, S.; Mottok, J.; Sedelmaier, Yvonne; Lehmann, Alexander; Landes, Dieter (2020)
Klopp, M.; Gold-Veerkamp, C.; Abke, J.; Borgeest, K.; Reuter, R.; Jahn, S.; Mottok, J....
Proceedings for the 4th European Conference on Software Engineering Education (ECSEE 2020) / New York, USA 2020, 12–21.
DOI: 10.1145/3396802.3396817
Peer Reviewed
Insights in Students’ Problems during UML Modelling.
Reuter, R.; Stark, T.; Sedelmaier, Yvonne; Landes, Dieter; Mottok, J.; Wolff, C. (2020)
Proceedings of the IEEE Global Engineering Education Conference (EDUCON 2020) 2020, 592–600.
DOI: 10.1109/EDUCON45650.2020.9125110
About the Effectiveness of Different Game Design Elements for an Introductory Programming Course.
Schwarzmann, Andreas; Landes, Dieter; Sedelmaier, Yvonne (2020)
49th International Conference on Engineering Pedagogy / 23rd International Conference on Interactive Collaborative Learning (ICL). 2020, 552–562.
Peer Reviewed
Using Learning Styles to Accommodate for Heterogeneous Groups of Learners in Software Engineering
Waibel, Nico; Sedelmaier, Yvonne; Landes, Dieter (2020)
In Proc. 11th IEEE Global Engineering Education Conference (EDUCON 2020), Porto, Portugal
2020, 819–826.
DOI: 10.1109/EDUCON45650.2020.9125233
Peer Reviewed
Impact of Generative Adversarial Networks on NetFlow-Based Traffic Classification
Wolf, Maximilian; Ring, M.; Landes, Dieter (2020)
13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020) / Cham 2020 (1267), 393–404.
Peer Reviewed
The Impact of Different System Call Representations on Intrusion Detection
Wunderlich, Sarah; Ring, M.; Landes, Dieter; Hotho, A. (2020)
Logic Journal of the IGPL 2020.
DOI: 10.1093/jigpal/jzaa058
Peer Reviewed
Flow-based network traffic generation using Generative Adversarial Networks
Ring, M.; Schlör, D.; Landes, Dieter; Hotho, A. (2019)
Computers & Security 82, 156–172.
DOI: 10.1016/j.cose.2018.12.012
Peer Reviewed
Better Understanding Fundamental Computer Science Concepts Through Peer Review
Sedelmaier, Yvonne; Landes, Dieter (2019)
The Challenges of the Digital Transformation in Education. Advances in Intelligent Systems and Computing 917, 64–75.
Peer Reviewed
Comparison of System Call Representations for Intrusion Detection
Wunderlich, Sarah; Ring, M.; Landes, Dieter; Hotho, A. (2019)
International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on European Transnational Education (ICEUTE 2019). Advances in Intelligent Systems and Computing 951, 14–24.
Peer Reviewed
A didactical concept for supporting reflection in software engineering education
Engelbrecht, L.; Landes, Dieter; Sedelmaier, Yvonne (2018)
Proceedings 9th IEEE Global Engineering Education Conference EDUCON 2018, 553–560.
DOI: 10.1109/EDUCON.2018.8363278
Peer Reviewed
Involving Customers in Requirements Engineering Education: Mind the Goals!
Hagel, G.; Müller-Amthor, M.; Landes, Dieter; Sedelmaier, Yvonne (2018)
Proceedings of the 3rd European Conference of Software Engineering Education ECSEE 2018, 113–121.
Peer Reviewed
Detection of slow port scans in flow-based network traffic
Ring, M.; Landes, Dieter; Hotho, A. (2018)
PLOS ONE 2018 13 (9).
DOI: 10.1371/journal.pone.0204507
Open Access Peer Reviewed
Frequently, port scans are early indicators of more serious attacks. Unfortunately, the detection of slow port scans in company networks is challenging due to the massive amount of network data. This paper proposes an innovative approach for preprocessing flow-based data which is specifically tailored to the detection of slow port scans. The preprocessing chain generates new objects based on flow-based data aggregated over time windows while taking domain knowledge as well as additional knowledge about the network structure into account. The computed objects are used as input for the further analysis. Based on these objects, we propose two different approaches for detection of slow port scans. One approach is unsupervised and uses sequential hypothesis testing whereas the other approach is supervised and uses classification algorithms. We compare both approaches with existing port scan detection algorithms on the flow-based CIDDS-001 data set. Experiments indicate that the proposed approaches achieve better detection rates and exhibit less false alarms than similar algorithms.
Active Learning of Software Quality and Project Management
Sedelmaier, Yvonne; Landes, Dieter (2018)
Proceedings 9th IEEE Global Engineering Education Conference EDUCON 2018, 1077–1085.
Peer Reviewed
Innovatives Requirements Engineering – ohne den Menschen?
Sedelmaier, Yvonne; Landes, Dieter (2018)
Softwaretechnik-Trends 38 (1), 35–36.
Peer Reviewed
Systematic evolution of a learning setting for requirements engineering education based on competence-oriented didactics
Sedelmaier, Yvonne; Landes, Dieter (2018)
Proceedings 9th IEEE Global Engineering Education Conference EDUCON 2018, 1068–1076.
DOI: 10.1109/EDUCON.2018.8363348
Peer Reviewed
Better Understanding Fundamental Computer Science Concepts through Peer Review
Sedelmaier, Yvonne; Landes, Dieter; Kuhn, Maria (2018)
47nd International Conference on Engineering Pedagogy / 21th International Conference on Interactive Collaborative Learning (ICL) 2018, 928–939.
Peer Reviewed
Fakultät Elektrotechnik und Informatik (FEI)
Hochschule Coburg
Friedrich-Streib-Str. 2
96450 Coburg
Monika Schnabel
Forschungsreferentin, EU-Referentin
T +49 9561 317 8062
monika.schnabel[at]hs-coburg.de