5 citations · 6 across the 3 of their papers we have counts for
5 papers
Spot the Difference: Detection of Topological Changes via Geometric Alignment
Steffen Czolbe, Aasa Feragen, Oswin Krause
Geometric alignment appears in a variety of applications, ranging from domain adaptation, optimal transport, and normalizing flows in machine learning; optical flow and learned aug…
Semantic similarity metrics for learned image registration
Steffen Czolbe, Oswin Krause, Aasa Feragen
We propose a semantic similarity metric for image registration. Existing metrics like Euclidean Distance or Normalized Cross-Correlation focus on aligning intensity values, giving…
Is segmentation uncertainty useful?
Steffen Czolbe, Kasra Arnavaz, Oswin Krause +1
Probabilistic image segmentation encodes varying prediction confidence and inherent ambiguity in the segmentation problem. While different probabilistic segmentation models are des…
DeepSim: Semantic similarity metrics for learned image registration
Steffen Czolbe, Oswin Krause, Aasa Feragen
We propose a semantic similarity metric for image registration. Existing metrics like euclidean distance or normalized cross-correlation focus on aligning intensity values, giving…
A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model
Steffen Czolbe, Oswin Krause, Ingemar Cox +1
To train Variational Autoencoders (VAEs) to generate realistic imagery requires a loss function that reflects human perception of image similarity. We propose such a loss function…