Frederic Guichard

Image Denoising Based on Adapted Dictionary Computation

This paper introduces a new denoising technique that consists in recovering the image using a filtering function adapted to the image content. The definition of such a function relies on the computation of similarity between pixels of a given neighborhood. Our contribution consists in the definition of a new similarity criterion which is more robust to noise. This measure is computed from a dictionary that is adapted to image content. The projection of the image content to this subspace are used then to define a metric between a pixel and the neighborhood ones. Very promising experimental results show the potential of our approach.

A morphological, affine, and Galilean invariant scale-space for movies

We study a model of multiscale analysis (or scale-space) applied to movies. This model comes from a thorough formalization that has been done in the theory of scale-space of static image. This formulation has led to associate with each multiscale analysis a partial differential equation (PDE). We intend here to examine the case of movies, and to insist on the motion aspects. More precisely, it has been proved in that there exists a unique affine and morphological and Galilean invariant scale-space for movies, the AMG model. This model is described by a partial differential equation. In this paper,we focus on terms appearing in that equation. We show that this model provides a reliable definition of an optical multiscale acceleration. At the practical level, scale is interpreted as a way of characterizing reliable trajectories. As we prove by experiments,the AMG model is a riddle for decimating spurious trajectories due to any kinds of nonadditive impurities and noise. Simple discrete formulae are given to implement the model.

Uniqueness of blur measure

After discussing usual approaches to measuring blur, we show theoretically that there is essentially a unique way to quantify blur by a single number and we confirm the usefulness of that measure by some experiment on a natural image.

Combined dynamic tracking and recognition of curves with application to road detection

We present an algorithm that extracts the largest shape within a specific class, starting from a set of image edgels. The algorithm inherits the best-first segmentation approach. However, instead of being applicable only to shapes defined within a given class of curves, we have extended our approach to tackle more general-and complex-shapes. For example, we can now process shapes obtained from sets defined over different kinds of curves and related to one another by estimated parameters. Therefore, we go from a segmentation problem to a recognition problem. In order to reduce the complexity of the searching algorithm, we work with a linearly parameterized class of shapes. This allows us, first, to use a recursive least-squares fitting, second, to cast the problem as the search of a largest edgel subset in a directed acyclic graph, and, third, to easily introduce a priori information on the location of the edgels of the searched subset. This leads us to propose a unified approach where recognition and tracking are combined. Experiments on recognizing and tracking both left and right road boundaries demonstrate that real-time processing is achievable.

Random walks, constrained multiple hypothesis testing and image enhancement

Image restoration is a keen problem of low level vision. In this paper, we propose a novel – assumption-free on the noise model – technique based on random walks for image enhancement. Our method explores multiple neighbors sets (or hypotheses) that can be used for pixel denoising, through a particle filtering approach. This technique associates weights for each hypotheses according to its relevance and its contribution in the denoising process. Towards accounting for the image structure, we introduce perturbations based on local statistical properties of the image. In other words, particle evolution are controlled by the image structure leading to a filtering window adapted to the image content. Promising experimental results demonstrate the potential of such an approach.

Accurate estimation of discontinuous optical flow by minimizing divergence related functionals

Classical definitions of the optical flow involve a conservation law, and a spatial smoothing of the velocities based on a norm of its gradient. We propose to use the divergence of the flow instead of using the gradient. So that the optical flow we obtained is the less divergent flow which satisfies the constraint defined by the conservation law. We propose also a model with occlusions, and show results of reconstructions based on the obtained estimation of the flow.

Multi-view reconstruction combining underwater and air sensors

The article describes an industrial application of a vision based 3D scene reconstruction process. The images come from independent cameras whose locations are unknown and which are placed indifferently in two different media (i.e. underwater or in the air). This process is currently used in the Poseidon system (see http://www.poseidon-tech.com) to realize an automatic CAD model of a swimming pool. Poseidon is the first computer system in the world to help in the prevention of drownings in public swimming pools. We have evaluated the reconstruction accuracy based on synthetic scenes as well as real ones. In the case of real data, results are compared with measurements made by a surveyor (with a telemeter) and demonstrate high accuracy in 3D localization and reconstruction. Lastly, it proves that computer vision modeling can be efficiently used for real time applications.

Image Reconstruction Using Particle Filters and Multiple Hypotheses Testing

In this paper, we introduce a reconstruction framework that explicitly accounts for image geometry when defining the spatial interaction between pixels in the filtering process. To this end, image structure is captured using local co-occurrence statistics and is incorporated to the enhancement algorithm in a sequential fashion using the particle filtering technique. In this context, the reconstruction process is modeled using a dynamical system with multiple states and its evolution is guided by the prior density describing the image structure. Towards optimal exploration of the image geometry, an evaluation process of the state of the system is performed at each iteration. The resulting framework explores optimally spatial dependencies between image content towards variable bandwidth image reconstruction. Promising results using additive noise models demonstrate the potentials of such an explicit modeling of the geometry.

Automatic detection of digital zooms

We propose and evaluate a method to determine whether a given digital image is the result of a digital zoom in or more generally of a linear interpolation. This information is important to some post-processing. It also sheds some light on the actual resolution of some digital cameras.

Software-based universal demultiplexing: threshold-free energy minimization approach

Software-based grouping of multiplexed video based on video content, as opposed to the signal generated by multiplexers, is described. The method is based on energy minimization approach. The algorithm automatically determines the amount of multiplexed camera views, and then the frames are grouped with respect to camera views. The algorithm is free of any threshold differences between camera views, and does not depend on the presence of quiet zones. The method also compensates for interference noise, local and global motion, are contrast changes.