Selected Publications

We present a method to extract per-point semantic class labels from aerial photogrammetry data. Unlike previous point cloud classification methods that rely exclusively on geometric features, we incorporate color information yielding a significant increase in accuracy. We test our classification method on three real-world photogrammetry datasets generated with Pix4Dmapper Pro, achieving good accuracy and generalization.
In ISPRS, 2017.

Local Binary Descriptors (LBDs) have become very popular for image matching tasks, especially when going mobile. However, their ability to carry enough information to infer the original image is seldom addressed. We leverage an inverse problem approach to directly reconstruct the image content from LBDs and without requiring either a prior learning database or non-binarized features. Interestingly, our reconstruction scheme reveals differences in the way different LBDs capture and encode image information.
In TPAMI, 2013.

Local Binary Descriptors (LBDs) are good at matching image parts, but how much information is actually carried? Surprisingly, this question is usually ignored and replaced by a comparison of matching performances. Here, we directly address it by trying to reconstruct plausible images from different LBDs such as BRIEF and FREAK and show that this task is achievable using only the information in the descriptors.
In ICPR, 2012.

Vision is a natural tool for human-computer interaction, since it provides visual feedback to the user. It requires however the fast and robust computation of motion primitives. We apply here some recent mathematical results about convex optimization to the TV-L1 optical flow problem. At the cost of a small smoothing of the Total Variation (TV), the convergence speed of the numerical scheme is improved, and realtime performance is achieved using the OpenCL framework.
In ICIP, 2009.

Recent Publications

. From bits to images: Inversion of local binary descriptors. In TPAMI, 2013.

Preprint PDF Code Project Project

. Fast TV-L 1 optical flow for interactivity. In ICIP, 2009.

Preprint PDF Project

Recent Posts

More Posts

Story of a bug that I introduced in my codebase while making it parallel.


A quick tip for people who have some troubles compiling OpenCV 3.0 alpha on MacOS X.


This is (finally) a follow-up to my latest post!


Ooops, it’s already end of May and… the first post for 2014.


The Non-Local Means algorithm 1 is a very popular image denoising algorithm. Besides its efficiency at denoising, it’s also one of the main causes to the non-locality trend that has arisen in Image Processing conferences ever since. Yet, there is this recent paper on Arxiv 2: Non-Local means is a local image denoising algorithm by Postec, Froment and Vedel. Some background on NL-means NL-means is a very simple, yet highly efficient image denoising algorithm that preserves well image textures even with severe noise.



Image reconstruction from Local Binary Descriptors

Inferring image content from local descriptors.


Continuing education

  • EUROSAE: MTS-014 Introduction to image manipulation. I am teaching 3 days (out of the 6) of this course directed by Bertrand Collin.

Academic courses

While a PhD student at EPFL’s Signal Processing Lab 2, I’ve been a TA for the following courses:

  • Introduction to Analog Signal Processing (SV and MT bachelors, duration 1 semester)
  • Introduction to Digital Signal Processing (SV and MT bachelors, duration 1 semester)
  • Advanced Signal Processing (EE master, duration 1 semester)

While in France, I’ve also given C/C++ programming labs for ENSTA-ParisTech new students.