2 citations · 4 across the 6 of their papers we have counts for
6 papers
Cracks in concrete
Tin Barisin, Christian Jung, Anna Nowacka +2
Finding and properly segmenting cracks in images of concrete is a challenging task. Cracks are thin and rough and being air filled do yield a very weak contrast in 3D images obtain…
Simulation of microstructures and machine learning
Katja Schladitz, Claudia Redenbach, Tin Barisin +5
Machine learning offers attractive solutions to challenging image processing tasks. Tedious development and parametrization of algorithmic solutions can be replaced by training a c…
Towards Generalized Entropic Sparsification for Convolutional Neural Networks
Tin Barisin, Illia Horenko
Convolutional neural networks (CNNs) are reported to be overparametrized. The search for optimal (minimal) and sufficient architecture is an NP-hard problem as the hyperparameter s…
Riesz feature representation: scale equivariant scattering network for classification tasks
Tin Barisin, Jesus Angulo, Katja Schladitz +1
Scattering networks yield powerful and robust hierarchical image descriptors which do not require lengthy training and which work well with very few training data. However, they re…
Riesz networks: scale invariant neural networks in a single forward pass
Tin Barisin, Katja Schladitz, Claudia Redenbach
Scale invariance of an algorithm refers to its ability to treat objects equally independently of their size. For neural networks, scale invariance is typically achieved by data aug…
Methods for segmenting cracks in 3d images of concrete: A comparison based on semi-synthetic images
Tin Barisin, Christian Jung, Franziska Müsebeck +2
Concrete is the standard construction material for buildings, bridges, and roads. As safety plays a central role in the design, monitoring, and maintenance of such constructions, i…