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Research ~ Methodologies Saturday, May 27th 2017, 00:52


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The project uses two broad classes of techniques to attack its problems :

In addition to applying these techniques to specific cases, the project advances these techniques more generally, through innovative modeling and theoretical analysis, and a comparative study of the two classes. An important recent theme, for example, is the incorporation of geometric information into both classes of techniques, in the probabilistic case via the use of stochastic geometry, and in the variational case via the use of higher-order active contours.

The project also concerns itself with a number of important, related problems, in particular the development of the parameter estimation procedures necessary to render the above methods automatic or semi-automatic, and the study of the optimization algorithms used to solve the problems (for example, reversible jump Markov chain Monte Carlo (RJMCMC)).

Probabilistic models

Following a Bayesian methodology as far as possible, probabilistic models are used within the Ariana project, as elsewhere, for two purposes : to describe the class of images to be expected from any given scene, and to describe prior knowledge about the scene in the absence of the current data.

Markov random fields

Markov random fields were introduced to image processing in the Eighties, and were quickly applied to the full range of inverse problems in computer vision. They owe their popularity to their flexible and intuitive nature, which makes them an ideal modeling tool, and to the existence of standard and easy-to-implement algorithms for their solution.

In the Ariana project, attention is focused on their use in image modeling, in particular of textures, on the development of improved prior models for segmentation, and on the lightening of the heavy computational load traditionally associated with these techniques, in particular via the study of varieties of hierarchical random field.


The development of wavelets as an alternative to the pixel and Fourier bases has had a big impact on image processing due to their spatial and frequency localization, and the sparse nature of many types of image data when expressed in these bases. In particular, wavelet bases have opened up many possibilities for probabilistic modeling due to the existence of not one but two natural correlation structures, intra- and inter-scale, leading to adaptive wavelet packet models and tree models respectively.

In Ariana, attention is focused on the use of tree models for denoising and deconvolution, adaptive wavelet packet models for texture description, and on the use of complex wavelets for their improved translation invariance and directional selectivity.

Stochastic geometry

One of the grand challenges of computer vision and image processing is the expression and use of prior geometric information. For satellite and aerial imagery, this problem has become increasingly important as the increasing resolution of the data results in the necessity to model geometric structures hitherto invisible. One of the most promising approaches to the inclusion of this type of information is stochastic geometry, which is a new and important line of research in the Ariana project.

Instead of defining probabilities for different types of image, probabilities are defined for configurations of an indeterminate number of interacting, parameterized objects located in the image. Such probability distribution are called "marked point processes". For instance, two examples that have been developed in Ariana use interacting cuboids of varying length, width, height and orientation for modeling buildings; and interacting line segments of varying length and orientation for modeling road and other networks.

Variational models
Regularization and functional analysis

The use of variational models for the regularization of inverse problems in image processing is long-established. Attention in Ariana is focused on the theoretical study of these models and their associated algorithms, and in particular on the convergence of sequences of functionals and on projection algorithms. Recent research concerns the definition and computation of a function space containing oscillatory patterns, a sort of dual space to the BV space that captures the geometry of the image. Variational methods are also applied to a variety of problems, including phase unwrapping and image decomposition.

Contours and regions

In addition to the regularization of inverse problems, variational methods are much used in the modeling of boundaries in images using contours. In Ariana, attention is focused on the use of such models for image segmentation, in particular texture segmentation; on the theoretical study of the models and their associated algorithms, in particular level set methods; and on the incorporation of prior geometric information concerning the regions sought using higher-order active contour energies.


Wavelets are important to variational approaches in two ways. They enter theoretically, through the study of Besov spaces, and they enter practically, in models of texture for segmentation, and in the denoising of the oscillatory parts of images.

Parameter estimation

One of the most important problems studied in the Ariana project is how to estimate the parameters that appear in the models. For probabilistic models, the problem is easily framed, but is not necessarily easy to solve, particularly in the case when it is necessary to extract simultaneously from the data both the information of interest and the parameters. For variational models, there are few methods available, and the problem is consequently more difficult.

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