3 citations · 5 across the 6 of their papers we have counts for
6 papers
Tracking Mouse from Incomplete Body-Part Observations and Deep-Learned Deformable-Mouse Model Motion-Track Constraint for Behavior Analysis
Olaf Hellwich, Niek Andresen, Katharina Hohlbaum +10
Tracking mouse body parts in video is often incomplete due to occlusions such that - e.g. - subsequent action and behavior analysis is impeded. In this conceptual work, videos from…
ConDL: Detector-Free Dense Image Matching
Monika Kwiatkowski, Simon Matern, Olaf Hellwich
In this work, we introduce a deep-learning framework designed for estimating dense image correspondences. Our fully convolutional model generates dense feature maps for images, whe…
Decomposer: Semi-supervised Learning of Image Restoration and Image Decomposition
Boris Meinardus, Mariusz Trzeciakiewicz, Tim Herzig +3
We present Decomposer, a semi-supervised reconstruction model that decomposes distorted image sequences into their fundamental building blocks - the original image and the applied…
DIAR: Deep Image Alignment and Reconstruction using Swin Transformers
Monika Kwiatkowski, Simon Matern, Olaf Hellwich
When taking images of some occluded content, one is often faced with the problem that every individual image frame contains unwanted artifacts, but a collection of images contains…
SIDAR: Synthetic Image Dataset for Alignment & Restoration
Monika Kwiatkowski, Simon Matern, Olaf Hellwich
Image alignment and image restoration are classical computer vision tasks. However, there is still a lack of datasets that provide enough data to train and evaluate end-to-end deep…
Content-Based Landmark Retrieval Combining Global and Local Features using Siamese Neural Networks
Tianyi Hu, Monika Kwiatkowski, Simon Matern +1
In this work, we present a method for landmark retrieval that utilizes global and local features. A Siamese network is used for global feature extraction and metric learning, which…