A framework to build a sparse representation of edges in images.
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PyPI version Research software impact

What is the SparseEdges package?

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 @ http://invibe.net/LaurentPerrinet/Publications/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 = {http://invibe.net/LaurentPerrinet/Publications/Perrinet15bicv}

This package gives a python implementation.

Moreover, it gives additional tools to compute useful stistics in images; first- and second order statistics of co-occurences 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