Downloads
Learning Convolutional Filters
Sample code for learning convolutional filters (MATLAB)
Pixel-wise medical image segmentation leveraging ad-hoc features with learned filters
Framework for learning a filter bank and perform pixel classification (C++/MATLAB)
Learning Separable Filters
Code for learning 2D separable filters and perform classification (MATLAB/C++)
Code for learning 3D separable filters and perform classification (MATLAB/C++)
Code for learning 2D separable filters with tensor decomposition (MATLAB)
Code for learning 3D separable filters with tensor decomposition (MATLAB)
Related Publications
Please note that the publication lists from Infoscience integrated into the EPFL website, lab or people pages are frozen following the launch of the new version of platform. The owners of these pages are invited to recreate their publication list from Infoscience. For any assistance, please consult the Infoscience help or contact support.
Learning Separable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015. Vol. 37, num. 1, p. 94-106. DOI : 10.1109/Tpami.2014.2343229.Please note that the publication lists from Infoscience integrated into the EPFL website, lab or people pages are frozen following the launch of the new version of platform. The owners of these pages are invited to recreate their publication list from Infoscience. For any assistance, please consult the Infoscience help or contact support.
On the Relevance of Sparsity for Image Classification
Computer Vision and Image Understanding. 2014. Vol. 125, p. 115-127. DOI : 10.1016/j.cviu.2014.03.009.Please note that the publication lists from Infoscience integrated into the EPFL website, lab or people pages are frozen following the launch of the new version of platform. The owners of these pages are invited to recreate their publication list from Infoscience. For any assistance, please consult the Infoscience help or contact support.
Accurate and Efficient Linear Structure Segmentation by Leveraging Ad Hoc Features with Learned Filters
2012. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France, October 1-5, 2012. p. 189-197. DOI : 10.1007/978-3-642-33415-3_24.Please note that the publication lists from Infoscience integrated into the EPFL website, lab or people pages are frozen following the launch of the new version of platform. The owners of these pages are invited to recreate their publication list from Infoscience. For any assistance, please consult the Infoscience help or contact support.
Are Sparse Representations Really Relevant for Image Classification?
2011. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2011), Colorado Springs, p. 1545-1552. DOI : 10.1109/CVPR.2011.5995313.Please note that the publication lists from Infoscience integrated into the EPFL website, lab or people pages are frozen following the launch of the new version of platform. The owners of these pages are invited to recreate their publication list from Infoscience. For any assistance, please consult the Infoscience help or contact support.
Filter Learning for Linear Structure Segmentation
2011
Contacts
| Roberto Rigamonti | [e-mail] |
| Amos Sironi | [e-mail] |
| Vincent Lepetit | [e-mail] |
| Pascal Fua | [e-mail] |
License
The code is released under the terms of the GNU General Public License (GPL), version 3.0.