4 citations · 14 across the 5 of their papers we have counts for
6 papers
ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate Particles
Max Coenen, Tobias Schack, Dries Beyer +2
We present a semi-supervised method for panoptic segmentation based on ConsInstancy regularisation, a novel strategy for semi-supervised learning. It leverages completely unlabelle…
Learning to Sieve: Prediction of Grading Curves from Images of Concrete Aggregate
Max Coenen, Dries Beyer, Christian Heipke +1
A large component of the building material concrete consists of aggregate with varying particle sizes between 0.125 and 32 mm. Its actual size distribution significantly affects th…
Semi-Supervised Segmentation of Concrete Aggregate Using Consensus Regularisation and Prior Guidance
Max Coenen, Tobias Schack, Dries Beyer +2
In order to leverage and profit from unlabelled data, semi-supervised frameworks for semantic segmentation based on consistency training have been proven to be powerful tools to si…
A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases
Chun Yang, Franz Rottensteiner, Christian Heipke
Land use as contained in geospatial databases constitutes an essential input for different applica-tions such as urban management, regional planning and environmental monitoring. I…
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching
Max Mehltretter, Christian Heipke
Due to its capability to identify erroneous disparity assignments in dense stereo matching, confidence estimation is beneficial for a wide range of applications, e.g. autonomous dr…
A two-layer Conditional Random Field for the classification of partially occluded objects
Sergey Kosov, Pushmeet Kohli, Franz Rottensteiner +1
Conditional Random Fields (CRF) are among the most popular techniques for image labelling because of their flexibility in modelling dependencies between the labels and the image fe…