Yannick Boursier

Spread spectrum for imaging techniques in radio interferometry

We consider the probe of astrophysical signals through radio interferometers with a small field of view and baselines with a non-negligible and constant component in the pointing direction. In this context, the visibilities measured essentially identify with a noisy and incomplete Fourier coverage of the product of the planar signals with a linear chirp modulation. In light of the recent theory of compressed sensing and in the perspective of defining the best possible imaging techniques for sparse signals, we analyse the related spread spectrum phenomenon and suggest its universality relative to the sparsity dictionary. Our results rely both on theoretical considerations related to the mutual coherence between the sparsity and sensing dictionaries and on numerical simulations.

Sparsity Driven People Localization with a Heterogeneous Network of Cameras

This paper addresses the problem of localizing people in low and high density crowds with a network of heterogeneous cameras. The problem is recast as a linear inverse problem. It relies on deducing the discretized occupancy vector of people on the ground, from the noisy binary silhouettes observed as foreground pixels in each camera. This inverse problem is regularized by imposing a sparse occupancy vector, i.e., made of few non-zero elements, while a particular dictionary of silhouettes linearly maps these non-empty grid locations to the multiple silhouettes viewed by the cameras network. The proposed framework is (i) generic to any scene of people, i.e., people are located in low and high density crowds, (ii) scalable to any number of cameras and already working with a single camera, (iii) unconstrained by the scene surface to be monitored, and (iv) versatile with respect to the camera’s geometry, e.g., planar or omnidirectional.Qualitative and quantitative results are presented on the APIDIS and the PETS 2009 Benchmark datasets. The proposed algorithm successfully detects people occluding each other given severely degraded extracted features, while outperforming state-of-the-art people localization techniques.

Sparsity-driven people localization algorithm: Evaluation in crowded scenes environments

We propose to evaluate our sparsity driven people localization framework on crowded complex scenes. The problem is recast as a linear inverse problem. It relies on deducing an occupancy vector, i.e. the discretized occupancy of people on the ground, from the noisy binary silhouettes observed as foreground pixels in each camera. This inverse problem is regularized by imposing a sparse occupancy vector, i.e. made of few nonzero elements, while a particular dictionary of silhouettes linearly maps these non-empty grid locations to the multiple silhouettes viewed by the cameras network. The proposed approach is (i) generic to any scene of people, i.e. people are located in low and high density crowds, (ii) scalable to any number of cameras and already working with a single camera, (iii) unconstraint on the scene surface to be monitored. Qualitative and quantitative results are presented given the PETS 2009 dataset. The proposed algorithm detects people in high density crowd, count and track them given severely degraded foreground silhouettes.

First K-Edge Imaging With a Micro-CT Based on the XPAD3 Hybrid Pixel Detector

We investigate the capability to perform K-edge imaging with the newly developed micro-CT PIXSCAN based on the XPAD3 hybrid pixel detector. The XPAD3 detector surface of 8 cm ×11 cm makes it possible to perform whole body mouse imaging. We present a proof of principle of K-edge imaging of mouse-size phantoms filled with Silver and Iodine solutions. Results are compared with standard X-ray absorption tomography for several solution densities.

A sparsity constrained inverse problem to locate people in a network of cameras

A novel approach is presented to locate dense crowd of people in a network of fixed cameras given the severely degraded background subtracted silhouettes. The problem is formulated as a sparsity constrained inverse problem using an adaptive dictionary constructed on-line. The framework has no constraint on the number of cameras neither on the surface to be monitored. Even with a single camera, partially occluded and grouped people are correctly detected and segmented. Qualitative results are presented in indoor and outdoor scenes.

Some proximal methods for CBCT and PET tomography

The reconstruction of the images obtained via the Cone Beam Computerized Tomography (CBCT) and Positron Emission Tomography (PET) Scanners are ill-posed inverse problems. One needs to adress carefully the problem of inversion of the mathematical operators involved. Recent advances in optimization have yielded efficient algorithms to solve very general classes of inverse problems via the minimization of non-differentiable convex functions. We show that such models are well suited to solve the CBCT and PET reconstruction problems. On the one hand, they can incorporate directly the physics of new acquisition devices, free of dark noise; on the other hand, they can take into account the specificity of the pure Poisson noise. We propose various fast numerical schemes to recover the original data and compare them to state-of-the-art algorithms on simulated data. We study more specifically how different contrasts and resolutions may be resolved as the level of noise and/or the number of projections of the acquired sinograms decrease. We conclude that the proposed algorithms compare favorably with respect to well-established methods in tomography.

Automatic detection of coronal mass ejections on LASCO-C2 synoptic maps

The LASCO-C2 coronagraph on-board the SOHO solar observatory has been providing a continuous flow of coronal images for the past nine years. Synoptic maps for each Carrington rotation have been constructed from these images and offer a global view of the temporal evolution of the solar corona, particularly the occurrence of transient events such as the coronal mass ejections (CMEs), an important component of space weather activity. CMEs present distinct signatures on synoptic maps offering a novel approach to the problem of their statistical detection. We are presently testing several techniques of automatic detection based on their morphological properties. The basic procedure involves three steps: i) morphological characterization, ii) definition and application of adapted filters (optimal trade-off filters, Canny filter,...), iii) segmentation of the filtered synoptic maps. At this stage, the CMEs are detected. The efficiency of the detection of the various filters is estimated using the ROC curves. On-going studies include the classification of CMEs based on their physical properties, the determination of their velocities, and the question of their connection to the streamer belt.

On the efficiency of proximal methods in CBCT and PET

Cone Beam Computerized Tomography (CBCT) and Positron Emission Tomography (PET) Scans are medical imaging devices that require solving ill-posed inverse problems. The models considered come directly from the physics of the acquisition devices, and take into account the specificity of the (Poisson) noise. We propose various fast numerical schemes to compute the solution. In particular, we show that a new algorithm recently introduced by A. Chambolle and T. Pock is well suited in the PET case when considering non differentiable regularizations such as total variation or wavelet ℓ1-regularization. Numerical experiments indicate that the proposed algorithms compare favorably with respect to well-established methods in tomography.

Implementing Geant4 on GPU for medical applications

Monte Carlo simulation (MCS) plays a key role in medical applications, especially for emission tomography (ET) and radiotherapy (RT). Unfortunately MCS is also associated with long calculation times that prevent for using it in routine clinical practice. Actually, a solution based on the use of computer clusters to solve the intensive computational issues is not realistic within routine clinical environment. Recently graphics processing units (GPU) became in many domains a cheap solution for the acquisition of a high power computation. The objective of this work was to develop an efficient framework for the implementation of MCS on GPU architectures. Geant4 was used as the MCS engine for targeting medical imaging and radiotherapy applications. We propose the definition of a global strategy and associated structures for such a GPU based simulation. The different steps needed for a Geant4 simulation were implemented on GPU. The first validations have shown equivalence in the underlying photon physics processes between the Geant4 and the GPU codes. Based on these simplistic simulations, we are expecting a speedup factor of over 200 for a complete simulation in emission tomography or in radiotherapy dosimetry.

Hybrid GATE: A GPU/CPU implementation for imaging and therapy applications

Monte Carlo simulations (MCS) play a key role in medical applications. In this context GATE is a MCS platform dedicated to medical imaging and particle therapy. Yet MCS are very computationally demanding, which limits their applicability in clinical practice. Recently, graphics processing units (GPU) became, in many domains, a cost-effective solution to access high power computation. The objective of this work was to develop a GPU code targeting MCS for medical applications integrated within the GATE software. An aim was to enhance GATE computational efficiency by taking advantage of GPU architectures. We first developed a GPU framework with basic elements to run MCS for different medical applications. The implementation was based on a GPU adaptation of the Geant4 code. For each main GATE medical application, we developed a specific code from the GPU framework. Some of these GPU codes are currently being integrated in GATE as new features, and users can perform GPU computing in their GATE simulations. The acceleration factor resulting from the implementation of the tracking within the phantom on GPU was 60 for a PET simulation and 80 for a CT simulation. By using GPU architectures, we are also extending GATE to support optical imaging simulations that are heavily demanding in terms of computational resources. Radiation therapy applications currently supported by GATE V6.2 are also being adapted to run on hybrid GPU/CPU architectures.