Seminars

Seminars which are organised by Ariana take place at INRIA Sophia Antipolis (map), both the precise location and the abstract being available as soon as possible. You can also consult previous years seminars' diary : 2024, 2023, 2022, 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999, and 1998.

Former seminars in project Ariana :

Title	Speaker	Date/Place	Abstract
Dual-tree M-band wavelet analysis; applications to image denoising	Caroline Chaux post-doc Ariana University of Marne La Vallée	12/18/2006 14h30 Coriolis
Abstract (english) : We propose here new wavelet-based geometric transforms: the dual-tree M-band wavelet analyses. These decompositions allow to perform a multi-scale, directional and local analysis of images. They are in the trend of recent works aiming at better representing geometric informations (textures, contours) and to perserve it while processing data. This work is based on previous works by N. Kingsbury and I. Selesnick who have obtained several results concerning the dyadic case. Their conclusions are extended to the M-band case. The proposed decompositions (based on two M-band wavelet transforms, the primal and the dual one, operating in parallel) typically introduce a redundancy of a factor 2 (4 in the complex case) and they constitute frames from which we can derive an optimal reconstruction. These new transforms have recently been generalized to biorthogonal and complex cases. We choose to apply these analysis tools to image denoising, which led us to study the statistical properties of coefficients generated by an M-band dual-tree analysis of a wide-sense stationary random process. Cross-correlations between primal and dual wavelets play a major role in our study. Numerical simulations allowed us to validate our theorical results as well as evaluate the influence area of the correlations. The effectiveness of our decompositions is demonstrated in image denoising. At first, we concentrate on mono-channel images to show that M-band dual-tree wavelet decompositions bring a significant gain in terms of both objective and subjective quality, with respect to a classical wavelet decomposition as well as a dyadic dual-tree wavelet decomposition. Then, we consider multichannel image denoising for which we build a new multivariate estimator based on Stein’s principle. The proposed estimator allows to take into account an arbitrary neighborhood (spatial, inter-component, inter-scale...).
Nice-levelable Markov random fields for SAR image restoration with preservation of contrast	Marc Sigelle Telecom Chief Engineer and Associate Professor ENST Paris	11/27/2006 14h30
Abstract (english) : It is now well-known that when an image containing a bright object within a dark background and overall Gaussian noise corruption is restored using Total Variation (TV) regularization [Rudin, Osher and Fatemi 1992], a significant loss of grey level contrast between recovered object and background can happen. We recently showed that TV is the paradigm of those regularization energy functionals which can be decomposed on level sets, which we called levelable functions [Darbon and Sigelle 2006]. We first show that for gaussian as well as for speckle noise and using levelable priors, the shape of the restored object does not change in a ``statistical'' sense, so that only its brightness decreases whereas that of background increases (loss of contrast). This extends the results of [Strong and Chan 2003]. We then show how to prevent the result to have this loss of contrast using nice-levelable priors. Some examples are given and appear concluding. This work is described in the research report [Darbon, Tupin and Sigelle 2006]. [Darbon, Tupin and Sigelle 2006] J. Darbon, M. Sigelle and F. Tupin A note on Nice-Levelable MRFs for SAR image denoising with contrast preservation. ENST Research report ENST 2006D006, September 2006. [Darbon and Sigelle 2006] J. Darbon and M. Sigelle. Image Restoration with Discrete Constrained Total Variation Part II: Levelable Functions, Convex Priors and Non-Convex Cases. Accepted for publication in Journal of Mathematical Imaging and Vision, March 2006. [Rudin, Osher and Fatemi 1992] L. Rudin, S. Osher and E.Fatemi. Nonlinear total variation based noise removal algorithms. Physica D. , Volume 60, pp. 259--268, 1992. [Strong and Chan 2003] D. Strong and T. Chan. Edge-preserving and Scale-dependent Properties of Total Variation Regularization. Inverse Problems, Volume 19, Number 6 (December 2003), pp. 165-187.
Prediction of forest structure parameters by textural analysis of very high resolution canopy images	Pierre Couteron, IRD – UMR AMAP (Montpellier) Institut Français de Pondichéry (IFP), India	11/17/2006 14h30 Coriolis
Abstract (english) : Predicting stand structure parameters for tropical forests from remotely sensed data has numerous important applications, as estimating aboveground biomass and carbon stocks and providing spatial information for forest mapping and management planning. We shall present the results obtained for both temporarily flooded and terra firme natural forests in French Guiana by applying an approach of "textural ordination" which ordinate or classify canopy image windows of adequate size on the basis of their Fourier spectra. Whatever the initial information used (IKONOS or digitized photograph contacts) the approach proved able to identify one or two prominent textural gradients, which were interpretable in terms of canopy coarseness/fineness with strong relationships with forest variations as observed from field measurements. Textural indices (i.e. windows scores along the gradients) were good predictors of forest parameters with reasonable estimation errors, and there are good prospects for broad-scale implementations. Potential improvements of the approach, which include standardization of spectra originating from images with heterogeneous technical features and fusion of "global" vs. "local" textural measures, will be discussed.
Wavelet-based multichannel image denoising and restoration	Amel Benazza Associate Professor SUP'COM, Tunis, Tunisia	11/13/2006 14h30 Coriolis
Abstract (english) : Multichannel imaging systems provide several observations of the same scene which are often corrupted by additive noise and blurred. In this work, we are interested both in multispectral image denoising and restoration in the wavelet domain. Concerning the denoising problem, we adopt a multivariate statistical approach in order to exploit the correlations existing between the different spectral components. Our main contribution is the application of Stein’s principle to build a new estimator for arbitrary multichannel images embedded in Gaussian noise. Simulation tests carried out on optical satellite images show that the proposed method outperforms conventional wavelet shrinkage techniques. Focusing on the problem of restoration, our approch has two main novelties. At the first step, we show how to combine M-band Wavelet Transforms (WT) with Fourier analysis to restore multicomponent images. At the second step, we point out that the multichannel deconvolution procedure takes advantage of exploiting multivariate regression rules. Simulations experiments carried out on multispectral satellite images indicate the good performance of our method.
Change Detection in Multitemporal Remote Sensing Images: Basics and Advanced Techniques	Lorenzo Bruzzone Professor, DIT University of Trento, Italy	10/16/2006 14h30 Coriolis
Abstract (english) : This talk presents the basics of change detection in multitemporal remote sensing images and the most recent developments in the context of unsupervised techniques for the automatic analysis of data. After introducing some preliminary concepts related to change detection in images acquired by both multispectral passive sensors and synthetic aperture radars, a novel theoretical framework for change vector analysis (CVA) in multispectral images will be presented and discussed. This framework, which is based on the representation of the CVA in polar coordinates, aims at: i) introducing a set of formal definitions in the polar domain (which are linked to the properties of the data) for a better general description (and thus understanding) of the information present in spectral change vectors; ii) analyzing from a theoretical point of view the distributions of changed and unchanged pixels in the polar domain (also according to possible simplifying assumptions); iii) driving the implementation of proper pre-processing procedures to be applied to multitemporal images on the basis of the results of the theoretical study! on the distributions; and iv) defining a solid background for the development of advanced and accurate automatic change-detection algorithms in the polar domain. Examples of application of the proposed framework (and of the related concepts) to real multispectral and multitemporal remote sensing images will be presented. The results obtained confirm the interest of the proposed framework and the validity of the related theoretical analysis.
The ETATS Project	Vinciane Lacroix Senior Researcher, Elec. Dept. Royal Military Academy, Brussels, Blegium	09/18/2006 14h30 Coriolis
Abstract (english) : In a near future, the National Geographic Institute of Belgium (NGI) will manage its vectorial data ranging from a conceptual scale of 1:10 000 to 1:50 000 in one single database. NGI is setting up a "planning tool" to schedule the data updating process according to the changes that occurred on the field and to compute the up-to-date status of the data as information to provide to end-users. The information about the changes will come from various sources, in particular, from remote sensed data. The choice of sensor is such that (i) the cost of a regular territorial coverage should be affordable; (ii) the regular territorial coverage should be technically possible; (iii) its resolution should enable the detection of changes in the built-up area and in the communication network. As a first step, a visibility test on SPOT5 panchromatic 5m resolution data fused with multi-spectral data has been performed to assess the possibility for a photo-interpretor to detect the buildings and the road network in various type of Belgian landscapes. As this test was positive, an automatic system could be envisaged. An automatic system to estimate the urbanization changes on the Belgian territory, using SPOT5 data and the National Geographic Institute vectorial database is thus proposed. The images and the vectorial data are first co-registered. Then, the vectorial database is projected and dilated to produce a mask representing the old status of the database. On the other hand, a fusion of two classification processes on the images enables to extract the built-up area and the communication network, generating a mask representing the actual state of the urbanization in the zone. The first process uses simplified Gabor filters to extract structures and texture, while the second is based on the vegetation index and the knowledge about hydrography. The comparison between the two masks provides coarse information on the changes. A validation of the process shows that the system provides about 20% of false alarms for an average of 92% of good detection.
Two-Step Iterative Shrinkage/Thresholding Algorithms for Total Variation and Wavelet-Based Image Restoration	Jose Manuel Bioucas Dias Associate Profesor, IST Technical University of Lisbon, Portugal	07/10/2006 14h30 Euler Indigo
Abstract (english) : Image restoration is usually formulated as the minimization of the sum of two convex functions: A quadratic data term and a nonquadratic regularizer (prior in the Bayesian framework). In recent work, a class of iterative denoising algorithms has been proposed. The denoising operator depends on the regularizer (prior). Popular regularizers are the 1) Total Variation (isotropic and nonisotropic), 2) the l^p norm, and 3) the p-th power of an l^p norm (both 2 and 3 with p greater ou equal to one). The first two classes are usually formulated in the image domain, whereas the former is often formulated in the wavelet domain in applications involving sparse representations. The iterative denoising approach is well suited to large scale problems. Its convergence rate is, however, overly slow when the linear operator associated with the data term is ill-conditioned or ill-posed. In this talk I will review this class of algorithms and present two-step (also known as second order) versions of the original ones that exhibit a much faster convergence rate. The underlying motivation behind the two-step versions parallels that of two-step linear methods to solve linear systems of equations. We show that the proposed two-step iterative scheme converges to a minimum of the underlying optimization problem, for a wide range of regularizers, including those mentioned above. We also give the optimal setting of the parameters that define the algorithm. The effectiveness of our scheme is illustrated with TV and wavelet-based image restoration examples.
Region-based extraction and analysis of visual object information	Ferran Marques Associate Professor UPC Barcelona, Spain	06/19/2006 14h30 Euler Indigo
Abstract (english) : Interaction with image database should be as user friendly as possible; that is, it should be as close as possible to the user's normal mode of communication. This concept is the basis for the so-called content-based image retrieval. To answer user's queries dealing with images containing a specific object, tools to detect the presence of such objects are necessary. We propose in this talk a strategy to detect objects from images. The approach relies on combining two types of models: a perceptual and a structural model. The algorithms that are proposed for both types of models make use of a region-based description of the image relying on a Binary Partition Tree. Perceptual models link the low-level signal description with semantic classes of limited variability. Given that we are dealing here with still images, the temporal evolution of low-level descriptors will not be considered to characterize the objects. Structural models represent the common structure of all instances! by decomposing the semantic object into simpler objects and by defining the relations between them.
Hybrid Imaging	Yoav Schechner Senior Lecturer, department of Electrical Engineering Technion, Israel	06/16/2006 14h30 Lagrange Gris
Abstract (english) : Rather than regarding images as given entities to be processed, richer information is gained by modifying and analyzing the imaging process itself. Such "hybrid imaging" exploits the power to affect both the sensor and the algorithmic components of a vision system. This is especially effective when having multiple sensing inputs. As examples, we briefly show how mosaicing can be generalized to overcome various radiometric problems (e.g., on-uniformity) or obtain high dynamic range, multispectral images. Then, we describe a bio-inspired method for recovering visibility in scattering media (haze,water). It is based on independent component analysis (ICA) of multiple frames, aided by a physical model of image formation. This model also easily leads to spatially varying regularization, overcoming inherent distance-dependent noise amplification. Finally, we address cross-modal scenarios: we seek correlating features between different inputs (or input-output), as done in climatology, medical research, and other fields. Here, we overcome insufficient data by a general mathematical principle, based on a sparsity prior.
Compressive Sampling	Robert-D. Nowak Associate Professor, Electrical and Computer Engineering University of Wisconsin-Madison, USA	05/29/2006 14h30 Coriolis
Abstract (english) : Compressive sampling (CS), or "Compressed Sensing", has recently generated a tremendous amount of excitement in the image processing community. CS involves taking a relatively small number of non-traditional samples in the form of randomized projections. Such samples are capable of capturing the most salient information in an image. If the image being sampled is compressible in a certain basis (e.g., wavelet), then under noiseless conditions the image can be much more accurately recovered from random projections than from pixel samples. We extended this type of result to show that compressible signals can be accurately recovered from random projections contaminated with noise, in many cases also much more accurately than is possible using an equivalent number of conventional point samples. I will review the basic theory and methods of CS, and discuss potential applications of CS in imaging and remote sensing. In particular, I will compare CS to conventional imaging by considering a canonical class of piecewise smooth image models. This analysis shows that CS can be advantageous in noisy imaging problems if the underlying image is highly compressible or if the signal-to-noise ratio is sufficiently large.
Statistical Analysis of Shapes of 2D Curves, 3D Curves, and Facial Surfaces, by Anuj Srivastava, Florida State University, USA	Anuj Srivastava Associate Professor, Dept. of Statistics Florida State University, USA	05/22/2006 14h30 Coriolis
Abstract (english) : In this talk, I will start by summarizing our previous work on techniques for computing geodesics between planar closed curves,with or without restrictions to arc-length parameterizations. Using tangent principal component analysis (TPCA) in this framework, we have imposed probability models on shape spaces and have used them in Bayesian shape estimation and classification. Extending these ideas to 3D problems, I will present a "path-straightening" approach for computing geodesics between closed curves in R3. The basic idea is to define a space of closed curves, initialize a path between the given two curves, and iteratively straighten this path using gradient of an energy whose critical points are geodesics. Lastly, I will explain how this computation, of geodesics between 3D curves, helps analyze shapes of facial surfaces. Using level sets of smooth functions, we represent a facial surface as an indexed collection of facial curves. Then, we compare any two facial surfaces by registering their facial curves, and by comparing shapes of corresponding curves.
Bayesian Denoising in Oriented and Non-Oriented Multi-Scale Pyramids	Jalal Fadili Associate Professor, Image and Signal Processing ENSI Caen, France	04/03/2006 14h30 Coriolis
Abstract (english) : Owing to recent advances in modern harmonic analysis, oriented (e.g. curvelets) and non-oriented (e.g. wavelets) multi-scale transforms have proven powerful in sparsely representing a wide class of images and signals (over some smoothness space classes). This work is concerned with bayesian denoising within the context of these sparse representations. A general univariate bayesian prior model, namely the scale mixture of gaussians, on the representation coefficients is introduced and its properties are established. We prove that such a prior is well adapted to capture sparsity of the representations (leptokurticity and heavy tailness). We also shed the light on the relationship between the hyperparameters of this prior and those of the Besov space within which realizations of such a prior are likely to fall. Several bayesian estimators (conditional posterior mean and maximum a posteriori) are also given. All these results are extended to the multivariate case where dependency between neighbouring coefficients in the multi-scale pyramid is imposed. Some special cases of the general prior are finally considered (e.g. the bessel K form) and their computational issues are solved.
On example based segmentation with wavelet features	Claire Gallagher PhD student, EEE Department Trinity College Dublin, Ireland	03/13/2006 14h30 Coriolis
Abstract (english) : The notion of example based image processing in general is one of increasing interest in the signal processing and computer vision community. The idea has long been a keystone in post-production houses and relies on an initial human generated estimate which is then propagated to other frames. In the image processing community this idea has recently been extended to improve automation. The best examples of this have been in example based texture synthesis (Efros et al. 1999) and Bayesian Matting (Chang et al. 2001,2002). This talk illustrates how the notion of gathering statistics from an image with the help of a human can be used for segmentation. The idea is to integrate the manually labelled example quantitatively into an inference framework for the segmentation of "related but not the same images". Complex wavelets are used to generate features that drive the segmentation process. As an example of the importance of using the complex wavelet, an improved version of the Efr! os technique is also demonstrated that is much more robust to scale. Example based segmentation holds great potential for putting the user quantitatively into the segmentation loop and the talk concludes with some interesting direction for applications.
Prior-based Segmentation by Projective Registration and Level Sets, by Tammy Riklin-Raviv	Tammy Raklin Raviv PhD student TAU, Tel Aviv, Israel	02/20/2006 14h30 Coriolis
Abstract (english) : Object detection and segmentation can be facilitated by the availability of a reference object. However, accounting for possible transformations between the different object views, as part of the segmentation process, remains a challenge. Recent works address this problem by using comprehensive training data. Other approaches are applicable only to limited object classes or can only accommodate similarity transformations. We suggest a novel variational approach to prior-based segmentation, which accounts for planar projective transformation, using a single reference object. The prior shape is registered concurrently with the segmentation process, without point correspondence. The algorithm detects the object of interest and correctly extracts its boundaries. The homography between the two object views is accurately recovered as well. Extending the Chan-Vese level set framework, we propose a region-based segmentation functional that includes explicit representation of the projective homography between the prior shape and the shape to segment. The formulation is derived from two-view geometry. Segmentation of a variety of objects is demonstrated and the recovered transformation is verified.
Finite states modeling for unsupervised texture segmentation	Giuseppe Scarpa Ercim Post-Doc Academy of Science, Czech Republic	02/20/2006 15h45 Coriolis
Abstract (english) : A novel color texture unsupervised segmentation algorithm is presented which processes independently the spectral and spatial information. The algorithm is composed of two parts. The former provides an over-segmentation of the image, such that basic components for each of the texture which are present are extracted. The latter is a region growing algorithm which reduces drastically the number of regions, and provides a region-hierarchical texture clustering. The over-segmentation is achieved by means of a color-based clustering (CBC) followed by a spatial-based clustering (SBC). The SBC, as well as the subsequent growing algorithm, make use of a characterization of the regions based on shape and context. Experimental results are very promising in case of textures which are quite regular.
SpaceFusion project: goals and preliminary results	André Jalobeanu CNRS Researcher LSIIT, ENSPS Strasbourg, France	02/13/2006 14h30 Coriolis
Abstract (english) : We propose to develop new multisource data fusion and reconstruction methods. The originality of the project lies in considering data fusion as the estimation of a single model, of arbitrary spatial and spectral resolutions. The model is to be inferred from a number of observations, possibly from different sensors under various conditions. It is all about reconstructing a geometric and radiometric object that best relates to the observations and integrates all the useful information contained in the initial data. In astronomical imaging, we will aim at a sharp, correctly sampled, noise-free and possibly super-resolved image. In the Virtual Observatory framework for instance, one wishes to combine large numbers of multispectral images from various sources. In planetary imaging or remote sensing, both terrain topography and camera parameters must be taken into account to efficiently combine several images. Therefore, the topography will be included in the model. The object provided by the fusion-reconstruction method will be a 3D surface, possibly super-resolved regarding both geometry and reflectance. The first objective is to provide a flexible framework for bandlimited signal reconstruction from multiple data. We obtained promising results in one dimension: a super-resolved signal was successfully reconstructed from two blurred and noisy shifted observations. In this framework, the sampling resolution, the geometric distortions, the blur kernel and the regularity of the sampling grid can be arbitrary for each sensor. The method was designed to handle realistic Gauss+Poisson noise and a simple Gaussian Markov chain was used for regularization purposes. We will also show new insights on the use of this Bayesian approach to infer a high-resolution deformation field from two satellite images.
A Markovian Approach on Foreground-Background-Shadow Segmentation of Video Images	Csaba Benedek PhD Student Hungaraian Academy of Science, MTA Sztaki, Hungary	02/06/2006 14h30 Euler violet
Abstract (english) : In the talk I introduce a new Markovian model regarding foreground and shadow detection in video sequences. The model works without detailed a priori object-shape information, and is also appropriate for low frame-rate video sources. I present three novelties: an improved shadow model, a new foreground calculus and a new integration technique of different color-texture features.