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)
Learning Separable FiltersIEEE Transactions on Pattern Analysis and Machine Intelligence. 2015. Vol. 37, num. 1, p. 94-106. DOI : 10.1109/Tpami.2014.2343229.
On the Relevance of Sparsity for Image ClassificationComputer Vision and Image Understanding. 2014. Vol. 125, p. 115-127. DOI : 10.1016/j.cviu.2014.03.009.
Accurate and Efficient Linear Structure Segmentation by Leveraging Ad Hoc Features with Learned Filters2012. 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.
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.
The code is released under the terms of the GNU General Public License (GPL), version 3.0.