Open Science

Apr 27, 2016

Snapshot of a Motion Cloud

To enable the dissemination of the knowledge that is produced in our lab, we share all source code with open source licences. This includes code to reproduce results obtained in papers (e.g. (Perrinet, Adams and Friston, 2015), (Perrinet and Bednar, 2015), (Khoei et, 2017), (Perrinet, 2019), (Pasturel et al, 2020), (Daucé et al, 2020)) or courses and slides (e.g. 2019-04-03: vision and modelization, 2019-04-18_JNLF, …) and also the development of the following libraries on GitHub.

Follow @laurentperrinet

bayesianchangepoint

An implementation of Adams & MacKay 2007 “Bayesian Online Changepoint Detection” for binary inputs in Python.

Source code
See the final publication @
Chloé Pasturel, Anna Montagnini, Laurent U Perrinet (2020). Humans adapt their anticipatory eye movements to the volatility of visual motion properties. PLoS Computational Biology.
Preprint PDF Cite Code DOI

ANEMO: Quantitative tools for the ANalysis of Eye MOvements

This implementation proposes a set of robust fitting methods for the extraction of eye movements parameters.

Source code
See a poster @ Pasturel, Montagnini and Perrinet (2018)

LeCheapEyeTracker

Work-in-progress : an eye tracker based on webcams.

Source code

Biologically inspired computer vision ( Python)

SLIP: a Simple Library for Image Processing

This library collects different Image Processing tools for use with the LogGabor and SparseEdges libraries.

LogGabor: a Simple Library for Image Processing

This library collects different Image Processing tools for use with the LogGabor and SparseEdges libraries.

SparseEdges: sparse coding of natural images

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/

SparseHebbianLearning : unsupervised learning of natural images

This is a collection of python scripts to test learning strategies to efficiently code natural image patches. This is here restricted to the framework of the SparseNet algorithm from Bruno Olshausen (http://redwood.berkeley.edu/bruno/sparsenet/).