Section: New Results

Fluid motion estimation

Stochastic uncertainty models for motion estimation

Participants : Shengze Cai, Etienne Mémin, Musaab Khalid Osman Mohammed.

The objective consists here in relying on a stochastic transport formulation to propose a luminance conservation assumption dedicated to the measurement of large-scale fluid flows velocity. This formulation, relying on the modeling under location uncertainty principle developed in the team, has the great advantage to incorporate from the beginning an uncertainty on the unresolved (turbulent) motion measurement. This uncertainty modeled as a possibly inhomogeneous random field uncorrelated in time can be estimated jointly to the motion estimates. Such a formulation, besides providing estimates of the velocity field and of its associated uncertainties, allows us to naturally define a linear multiresolution scale-space framework. It provides also a reinterpretation, in terms of uncertainty, of classical regularization functionals proposed in the context of motion estimation. Nevertheless, at variance to classical motion estimation methods, this approach enables to estimate the so-called regularization parameter, which is in our framework related to the variance of the unresolved component of motion component. The resulting parameter-free estimator has shown to outperform state-of-the-art results of the literature [14]. This kind of method is applied on turbulent flows and in the context of river hydrologic applications through a collaboration with the Irstea Lyon research group (HHLY). A method for the 3D reconstruction of the river plane has been also proposed in this context.This study is performed within the PhD thesis of Musaab Mohammed.

Surface Currents estimation from Shore-Based Videos

Participant : Pierre Derian.

A wavelet based motion estimator has been extended for the recovery of instantaneous fields of surface current from shore-based and unmanned aerial vehicle videos. This study published in [16] and [34] demonstrated clearly the high potential of this method in the nearshore, where the rapid development of webcams and drones offers a large amount of applications for swimming and surfing safety, engineering and naval security and research purpose, by providing quantitative information. This work has been conducted within a collaboration with the Legos laboratory.

Development of an image-based measurement method for large-scale characterization of indoor airflows

Participants : Dominique Heitz, Etienne Mémin, Romain Schuster.

The goal is to design a new image-based flow measurement method for large-scale industrial applications. From this point of view, providing in situ measurement technique requires: (i) the development of precise models relating the large-scale flow observations to the velocity; (ii) appropriate large-scale regularization strategies; and (iii) adapted seeding and lighting systems, like Hellium Filled Soap Bubles (HFSB) and led ramp lighting. This work conducted within the PhD of Romain Schuster in collaboration with the compagny ITGA has started in february 2016. The first step has been to evaluate the performances of a stochastic uncertainty motion estimator when using large scale scalar images, like those obtained when seeding a flow with smoke. The PIV characterization of flows on large fields of view requires an adaptation of the motion estimation method from image sequences. The backward shift of the camera coupled to a dense scalar seeding involves a large scale observation of the flow, thereby producing uncertainty about the observed phenomena. By introducing a stochastic term related to this uncertainty into the observation term, we obtained a significant improvement of the estimated velocity field accuracy [41].

3D flows reconstruction from image data

Participants : Dominique Heitz, Cédric Herzet.

Our work focuses on the design of new tools for the estimation of 3D turbulent flow motion in the experimental setup of Tomo-PIV. This task includes both the study of physically-sound models on the observations and the fluid motion, and the design of low-complexity and accurate estimation algorithms.

This year, we continued our investigation on the problem of efficient volume reconstruction. During the last years, we have focussed our attention on several families of convex optimization algorithms allowing to accelerate the standard procedures encountered in the Tomo-PIV literature while accounting for the non-negativity and the sparsity of the sought solutions. So far, the assessment of the proposed algorithms were exclusively done on synthetic data. This year, we started the process of validating the proposed procedures on real experimental data.

We started through a collaboration with Irstea to study ensemble assimilation methods for the fast reconstruction of 3D tomo-PIV motion field. The approach relies on a simplified dynamics of the flow and is a generalization of one of the popular emerging flow reconstruction technique of the PIV community referred to as "Shake the box". The study will be developed within an Irstea post-doctoral funding.

Sparse-representation algorithms

Participant : Cédric Herzet.

The paradigm of sparse representations is a central concept in many domains of signal processing. In particular, in the field of fluid motion estimation, sparse representation appears to be potentially useful at several levels: (i) it provides a relevant model for the characterization of the velocity field in some scenarios; (ii) it plays a crucial role in the recovery of volumes of particles in the 3D Tomo-PIV problem.

Unfortunately, the standard sparse representation problem is known to be NP hard. Therefore, heuristic procedures have to be devised to access to the solution of this problem. This year, we continued our investigations on “screening’’ methodologies, that is procedures allowing for the rapid identification of (some of) the zeros of the sought sparse vector. More specifically, we designed low-complexity procedures enabling to screen groups of atoms by only performing one single test. This work has been submitted to the IEEE international conference on acoustic, speech and signal processing (ICASSP).