Optical Flow

Optical Flow Compression Code

The matlab code for computing optimal optical flow masks for compression is avaible here. Note that the code is hardly documented and provided “as-is”. The code is released under a GPL v3 licence (unless specified differently).

Towards PDE-Based Video Compression with Optimal Masks Prolongated by Optic Flow

Lossy image compression methods based on partial differential equations have received much attention in recent years. They may yield high-quality results but rely on the computationally expensive task of finding an optimal selection of data. For the …

Optical Flow Computation

I’ve developed optimization algorithms for variational optical flow models based on the split Bregman algorithm in my Master thesis. A follow-up investigation on the necessity of certain intermediate filtering steps was published at the EMMCVPR 2011.

Towards PDE-Based Video Compression with Optimal Masks and Optic Flow

Lossy image compression methods based on partial differential equations have received much attention in recent years. They may yield high quality results but rely on the computationally expensive task of finding optimal data. For the possible …

Bregman Iteration for Correspondence Problems: A Study of Optical Flow

Bregman iterations are known to yield excellent results for denoising, deblurring and compressed sensing tasks, but so far this technique has rarely been used for other image processing problems. In this paper we give a thorough description of the …

Bregman Iteration for Optical Flow

Oral presentation

Intermediate Flow Field Filtering in Energy Based Optic Flow Computations

The Euler-Lagrange framework and splitting based methods are among the most popular approaches to solve variational optic flow problems. These methods are commonly embedded in a coarse-to-fine strategy to be able to handle large displacements. While …

Intermediate Flow Field Filtering in Energy Based Optic Flow Computations

Poster presentation

Bregman Iteration for Optical Flow

Osher and his colleagues introduced Bregman iterations in image processing in 2005. This technique is known to yield excellent results for denoising/deblurring and compressed sensing tasks but it has so far been rarely used for other image …