activity
19952019
most citedThe Random Forest Classifier in WEKA: Discussion and New Developments for Imbalanced Data

15 citations · 36 across the 8 of their papers we have counts for

collaborators

9 papers

cs.HC2019

A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems

Mario Amrehn, Stefan Steidl, Reinier Kortekaas +4

For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small am…

cs.CL2019★ 4 cited

On Laughter and Speech-Laugh, Based on Observations of Child-Robot Interaction

Anton Batliner, Stefan Steidl, Florian Eyben +1

In this article, we study laughter found in child-robot interaction where it had not been prompted intentionally. Different types of laughter and speech-laugh are annotated and pro…

cs.CV2019★ 15 cited

The Random Forest Classifier in WEKA: Discussion and New Developments for Imbalanced Data

Mario Amrehn, Firas Mualla, Elli Angelopoulou +2

Data analysis and machine learning have become an integrative part of the modern scientific methodology, providing automated techniques to predict further information based on obse…

cs.CV2017

Precision Learning: Towards Use of Known Operators in Neural Networks

Andreas Maier, Frank Schebesch, Christopher Syben +4

In this paper, we consider the use of prior knowledge within neural networks. In particular, we investigate the effect of a known transform within the mapping from input data space…

cs.CV2017★ 2 cited

Robust Seed Mask Generation for Interactive Image Segmentation

Mario Amrehn, Stefan Steidl, Markus Kowarschik +1

In interactive medical image segmentation, anatomical structures are extracted from reconstructed volumetric images. The first iterations of user interaction traditionally consist…

cs.CV2017★ 12 cited

UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model

Mario Amrehn, Sven Gaube, Mathias Unberath +6

For complex segmentation tasks, fully automatic systems are inherently limited in their achievable accuracy for extracting relevant objects. Especially in cases where only few data…