Our goal here is to build practical algorithms of sparse coding for computer vision.
This class exploits the SLIP and LogGabor libraries to provide with a sparse representation of edges in images.
This algorithm was presented in the following paper, which is available as a reprint @ https://laurentperrinet.github.io/publication/perrinet-15-bicv/ :
@inbook{Perrinet15bicv,
author = {Perrinet, Laurent U.},
booktitle = {Biologically-inspired Computer Vision},
chapter = {13},
citeulike-article-id = {13566753},
editor = {Keil, Matthias and Crist\'{o}bal, Gabriel and Perrinet, Laurent U.},
publisher = {Wiley, New-York},
title = {Sparse models},
year = {2015},
url = {https://laurentperrinet.github.io/publication/perrinet-15-bicv}
}
This package gives a python implementation.
Moreover, it gives additional tools to compute useful statistics in images; first- and second order statistics of co-occurrences in images. More information is available @ http://nbviewer.ipython.org/github/bicv/SparseEdges/blob/master/SparseEdges.ipynb Tests for the packages are available @ http://nbviewer.ipython.org/github/bicv/SparseEdges/blob/master/notebooks/test-SparseEdges.ipynb