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) or courses and slides (e.g. 2019-04-03_a_course_on_vision_and_modelization, 2019-04-18_JNLF, …) and also the development of the following libraries on GitHub.
An implementation of Adams & MacKay 2007 “Bayesian Online Changepoint Detection” for binary inputs in Python.
Work-in-progress : an eye tracker based on webcams.
Biologically inspired computer vision
SLIP: a Simple Library for Image Processing
LogGabor: a Simple Library for Image Processing
SparseEdges: sparse coding of natural images
Our goal here is to build practical algorithms of sparse coding for computer vision.
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/).
MotionClouds are random dynamic stimuli optimized to study motion perception.