Besides my research in computer vision related tasks such as optical flow, photometric stereo, and shape matching and my focus on PDE-based compression, I have also ventured in other image processing tasks.
I have used machine learning techniques in various projects. Our most successful applications were in the context of quantizing colour values for optimized inpainting and in accoustic source characterizations.
Source Code The source code for clustering methods used for quantizing optimal masks can be found here.
Over the years I have written quite a lot of Matlab code. The code is available here, here, here, and here and covers mostly image processing and interpolation/approximation topics. All the code is released under a GPL v3 licence (unless specified differently).
Over the years I have written quite a lot of Mathematica code. The code is available here and here and covers mostly image processing topics. All the code is released under a GPL v3 licence (unless specified differently).
Floating point computations on computers may behave differently than one might expect. Every software developer should be aware of these since computed results may be off by orders of magnitude in the worst case.
I develop a small toy setup with a pair of Arduinos and some ultrasound sensors to do object localization. The goal is to have at least 4 echo distances in each measurement to assert a unique solution of the localization equations.
We have investigated high performing optimization algorithms and matrix differential calculus technique in the context of Photometric Stereo and presented the results at the BMVC 2016
Source Code A github repository with the code is maintained by Yvain Quéau.