T
Thomas Brox
Researcher at University of Freiburg
Publications - 353
Citations - 127470
Thomas Brox is an academic researcher from University of Freiburg. The author has contributed to research in topics: Segmentation & Optical flow. The author has an hindex of 99, co-authored 329 publications receiving 94431 citations. Previous affiliations of Thomas Brox include Dresden University of Technology & University of California, Berkeley.
Papers
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Book ChapterDOI
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Özgün Çiçek,Ahmed Abdulkadir,Ahmed Abdulkadir,Soeren S. Lienkamp,Thomas Brox,Olaf Ronneberger,Olaf Ronneberger +6 more
TL;DR: In this paper, the authors propose a network for volumetric segmentation that learns from sparsely annotated volumetrized images, which is trained end-to-end from scratch, i.e., no pre-trained network is required.
Proceedings ArticleDOI
FlowNet: Learning Optical Flow with Convolutional Networks
Alexey Dosovitskiy,Philipp Fischery,Eddy Ilg,Philip Häusser,Caner Hazirbas,Vladimir Golkov,Patrick van der Smagt,Daniel Cremers,Thomas Brox +8 more
TL;DR: In this paper, the authors propose and compare two architectures: a generic architecture and another one including a layer that correlates feature vectors at different image locations, and show that networks trained on this unrealistic data still generalize very well to existing datasets such as Sintel and KITTI.
Proceedings Article
Striving for Simplicity: The All Convolutional Net
TL;DR: It is found that max-pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks.