Franz Köferl
Franz Köferl, M. Sc.
Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik
Carl-Thiersch-Straße 2b
91052 Erlangen
- Phone number: +49 9131 85-20284
- Email: franz.koeferl@fau.de
- Website: https://www.mad.tf.fau.de/person/franz-koeferl/
Academic CV
| Since 7/2017 | PhD Candidate
Machine Learning and Data Analytics Lab, |
| 5/2014 – 2/2017 | M.Sc. in Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Master Thesis : “Segmentation and Classification of Interlinear and Marginal Glosses using Convolutional Neural Networks“ |
| 7/2016 – 6/2017 | Student/Assistant Researcher at Fraunhofer IIS |
| 5/2011 – 5/2014 |
B.Sc. in Computer Science
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Bachelor Thesis: “Experimentelle Untersuchung des Einflusses von Bound-Handling-Strategien auf die Partikel-Verteilung bei der Partikelschwarmoptimierung.” |
Research Projects
Demos
Demonstrator Deep Learning
Machine Learning, especially Deep Learning are complex methods for solving various problems, ranging from detection of persons to predicting the time for maintenance of specific parts of a machine. The methods struggle with intuition, when you can solve or when you can’t apply these methods to solve a specific task. This demo demonstrates the conditions needed for a reliable application of deep learning methods and their respective results. Have look at our poster.
The following videos demonstrate an Object Detector Yolov3 [1] trained on naive data, augmentated and simulated from our demo machine. The used data consists of ca. 40,000 images, each manually labeled.
- Trained without any measure
Trained with patch augmentationTrained with simulated images
[1] Joseph Redmon, Ali Farhadi: YOLOv3: An Incremental Improvement.
Publications
2023
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Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks
2023 International Joint Conference on Neural Networks (IJCNN) (Gold Coast, Australia, 18. June 2023 - 23. June 2023)
In: Proc. Intl. Joint Conf. Neural Netw. (IJCNN) 2023
DOI: 10.1109/IJCNN54540.2023.10191724
BibTeX: Download - , , , , , , , , :
PolarBearVidID: A Video-Based Re-Identification Benchmark Dataset for Polar Bears
In: Animals 13 (2023), p. 801
ISSN: 2076-2615
DOI: 10.3390/ani13050801
BibTeX: Download
2020
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Application of SORT on Multi-Object Tracking and Segmentation
Conference on Computer Vision and Pattern Recognition; 5th BMTT MOTChallenge Workshop: Multi-Object Tracking and Segmentation (Seattle, WA, USA (Virtual), 19. June 2020 - 19. June 2020)
URL: https://motchallenge.net/workshops/bmtt2020/papers/Application_and_Adaptations_of_SORT_on_MOTS20.pdf
BibTeX: Download
(Working Paper) - , , , :
Interactive Segmentation of RGB-D Indoor Scenes using Deep Learning
International Conference on Machine Learning; 2nd ICML 2020 Workshop on Human in the Loop Learning (Virtual Conference, 18. July 2020 - 18. July 2020)
BibTeX: Download
(Conference report)
2015
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Lightweight, generative variant exploration for high-performance graphics applications
14th International Conference on Generative Programming: Concepts & Experiences (GPCE) (Pittsburgh, PA, 26. October 2015 - 27. October 2015)
In: ACM Bd. 51, Nr. 3 2015
DOI: 10.1145/2814204.2814220
BibTeX: Download
Partners & Funding Agencies
