David Stutz, Matthias Hein, Bernt Schiele.
Disentangling Adversarial Robustness and Generalization.
ArXiv, 2018.
[ArXiv | BibTeX | Project Page]
David Stutz, Andreas Geiger.
Learning 3D Shape Completion under Weak Supervision.
International Journal of Computer Vision, 2018.
[DOI | ArXiv | BibTeX | Project Page]
David Stutz, Andreas Geiger.
Learning 3D Shape Completion from Laser Scan Data with Weak Supervision.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[PDF | BibTeX | Project Page]
David Stutz, Alexander Hermans, Bastian Leibe.
Superpixels: an evaluation of the state-of-the-art.
Computer Vision and Image Understanding, Volume 166, 2018.
[DOI | ArXiv | PDF | BibTeX | Project Page]
David Stutz.
Superpixel segmentation: an evaluation.
German Conference on Pattern Recognition, 2015.
[PDF | BibTeX | Project Page]
Revised C++ implementations of two popular superpixel algorithms, SEEDS and FH, which are shown to outperform the original implementations.
In the course of the last couple of semesters, I extended the initial comparison of superpixel algorithms in my bachelor thesis to a comprehensive comparison of 28 state-of-the-art algorithms on 5 datasets with regard to quantitative and qualitative performance. The results are now available on ArXiv.
A comprehensive comparison and evaluation of 28 superpixel algorithms on 5 different datasets; published in CVIU and GCPR.
After completing my bachelor thesis, I was encouraged to submit the results at the Young Researcher Forum of the German Conference on Pattern Recognition (GCPR) 2015. In this article, I want to share the paper as well as the corresponding poster.
A comparison of several state-of-the-art superpixel algorithms using an extended version of the Berkeley Segmentation Benchmark.