Titles and abstracts
- Hermine Biermé and Agnès Desolneux, Université Paris Descartes
Title: ``Shot noise models: applications and properties''
Shot noise models, also sometimes called "filtered Poisson
processes", are defined as real-valued random fields, where the value
at each point is the sum of the contributions of a (deterministic)
kernel function centered at the points of a (random) Poisson point
Our talk will be divided in two parts. In the first part, we will
review applications of shot noise random fields in Physics and in
Image Processing, mainly for texture synthesis. In the second part, we
will study the properties of shot noise random fields and in the 1D
case, we will describe the crossings of such a process. In particular
we will be interested in the three following questions: 1) describing
the behavior of the shot noise random field as the intensity of the
underlying Poisson point process goes to infinity; 2) giving an
explicit formula for the crossings in the 1D case; and 3) studying
the particular case where the kernel function is a 1D Gaussian.
- Toni Buades, Université Paris Descartes and CNRS
Title: ``A note on multi-image denoising''
Taking photographs under low light conditions with a hand-held
camera is problematic. A long exposure time can cause motion blur
due to the camera shaking and a short exposure time gives a noisy
image. We consider the new technical possibility offered by cameras
that take image bursts. Each image of the burst is sharp but noisy.
In this preliminary investigation, we explore a strategy to
efficiently denoise multi-images or video. The proposed algorithm
is a complex image processing chain involving accurate
registration, video equalization, noise estimation and the use of
state-of-the-art denoising methods. Yet, we show that this complex
chain may become risk free thanks to a key feature: the noise model
can be estimated accurately from the image burst. Preliminary tests
will be presented. On the technical side, the method can already be
used to estimate a non parametric camera noise model from any image
- Serge Cohen, Synchrotron Soleil
Title: ``Unsupervised segmentation of
images from spectro-microscopy based on
gaussian mixture model and model selection''
Spectro-microscopy produce images composed of hundreds to thousands pixels, each pixel being characterised by a hi-resolution spectra, that is a curve of around one thousand values. The aim of this work is to perform a segmentation of the image based not only on similarities of spectra measured on each pixel, but also the spatial proximity of pixels. Here we propose to use a model selection approach where the models represent simultaneously the population of observed spectra and the segmentation of the image. The criteria is a penalised likelihood, where the penalty depends both on the number and parametrisation of cluster of spectra and on the complexity of the segmentation.
Practically, to perform the segmentation, an initial non-parametric dimension reduction step with a "best approximation" target is taken. Then we carry out a Gaussian mixture modelling of the spectra, using a modified EM algorithm that performs segmentation as part of the M step, where the segmentation optimises a penalised likelihood target favouring simple segmentation. Finally a model selection step is conduced, to find the optimal number of clusters in the image (and possibly selecting the discriminating variables).
joint work with E. Le Pennec
- Bartomeu Coll, Universitat de les Illes Balears, Spain
Title: ``Impact of the compression in the stereo problem''
Recent Earth observation satellite projects, in particular the Pleiades
project (to be launched in 2010) contemplate the acquisition of
quasi-simultaneous stereo pairs. This paper evaluates the impact of the
(necessary) compression of the stereo pairs. It compares two compression
strategies. The first compression strategy uses classic JPEG 1992 or
JPEG 2000, which retain the best perceptual performance. The second
compression strategy maintains a shift invariance by simply sub-sampling
both views after applying an anti-aliasing Gaussian filter. The
quantitative comparison of these two basic strategies shows that JPEG
algorithms must compress twice less than sub-sampling to reach the same
disparity precision. This dramatic result is explained by the lack of
translation invariance of classic compression algorithms. Nonetheless,
the sweeping conclusion is that shift invariant algorithms are better
compression tools for future stereo Earth observation satellites.
authors: G. Blanchet, A. Buades, B. Coll, J.-M. Morel and B. Rougé
- Baptiste Coulange, Université Paris Descartes
Title: ``An aliasing detection algorithm based on suspicious colocalizations of Fourier coefficients''
We propose a new algorithm to detect the presence and the localization of aliasing in a single image. Considering images in Fourier domain, the fact that two frequencies in aliasing relation contribute to similar parts of the image domain is a suspicious coincidence we detect with an a-contrario model. This leads to a localization of the aliasing phenomenon in both spatial and spectral domains, with a detection algorithm that keeps control of the number of false alarms. Experiments on several images show that this new method favorably compares to the state of the art, and opens interesting perspectives in terms of image enhancement.
- Arnak Dalalyan, École des Ponts ParisTech
Title: ``Robust Estimation for an Inverse Problems Arising in Multiview Geometry''
We propose a new approach to the problem of robust estimation for a
class of inverse problems arising in multiview geometry. Inspired by
recent advances in the statistical theory of recovering sparse
vectors, we define our estimator as a Bayesian maximum a posteriori
with multivariate Laplace prior on the vector describing the outliers.
This leads to an estimator in which the fidelity to the data is
measured by the L∞- norm while the regularization is done by the
L1-norm. The proposed procedure is fairly fast since the outlier
removal is done by solving one linear program (LP). An important
difference compared to existing algorithms is that for our estimator
it is not necessary to specify neither the number nor the proportion
of the outliers; only an upper bound on the maximal measurement error
for the inliers should be specified. We present theoretical results
assessing the accuracy of our procedure, as well as numerical examples
illustrating its efficiency on synthetic and real data.
- Xavier Descombes, INRIA Sophia Antipolis
Title: ``Marked point processes for image analysis''
Image sensors provide now high or very high resolution images. At this
scale, the information embedded by radiometric or
texture property is not sufficient to fully exploit the image content.
Indeed, the geometric information becomes a key feature
to describe these images. On the other, stochastic models, and
especially Markov Random Fields, have proved to be a powerful
framework for analyzing images. These models are mostly defined at a
pixel level and are adapted to represent the contextual information.
Modeling geometry by considering local potential function is hardly
feasible. Markov Random Fields on graph may be a solution but they require
the definition of graoh, which how mayn objects there are in the scene
and what is there distribution in the space. This data means to almost
problem of image analysis. To overcome this limit, we propose to
consider marked point process for which an object is associated to each
in the configuration, the number of objects being random. In this talk,
we will consider the different aspects of the approach, that is
modeling, optimizing and parameter estimating. Some examples on object
detection will be given (tree, roads, flamingos,...)
- Bruno Galerne, École Normale Supérieure de Cachan
Title: ``Transparent dead leaves process''
Several classic random field models are defined in combining random
objects according to a superimposition principle (e.g. linear
superimposition for shot noise models, occlusion for the colored
dead leaves model).
In this talk we define and study the transparent dead leaves process
(TDL process), a random field obtained in sequentially superimposing
random transparent objects. Basic statistics of this new model are
derived as well as a simulation algorithm. The influence of the
transparency coefficient of the objects is then detailed. When the
random objects are opaque, the TDL process is the randomly colored
dead leaves model. On the other extreme, one shows that when the
objects tends to be fully transparent, the (normalized) TDL process
tends towards a Gaussian random field. In all the other cases, the
TDL process is a random field with bounded variation having
discontinuities almost everywhere.
- Donald Geman, Johns Hopkins University, USA
Title: ``A Synthetic Visual Reasoning Test''
I will discuss a new challenge for machine learning and computer
vision that Francois Fleuret and I have designed. The SVRT consists
of a series of 23 hand-designed, image-based, binary classification
problems. For each task there is a generator in C++ which allows one
to produce as many i.i.d samples as desired. Our intention is to
expose some limitations of current methods for pattern recognition,
and to argue for making a larger investment in other paradigms and
strategies, emphasizing the pivotal role of relationships among parts,
complex hidden states and a rich dependency structure. Another
motivation is to compare the performance of humans and machines (and
possibly monkeys). The human experiments are already complete, and
were conducted in the laboratory of Prof. Steven Yantis, a cognitive
psychologist at Johns Hopkins University. Baseline experiments with
machine learning methods are underway and there is also an official
"competition" (in fact a "Pascal Challenge") intended accurately asses
the state-of-the-art of machine learning methods.
- Joan Glaunès, Université Paris Descartes
Title: ``Distributions for modelling sub-manifolds and applications to template estimation''
Currents are generalizations of mathematical distributions which can represent
sub-manifolds of arbitrary dimension in euclidean space. They allow to
represent mathematically curves and surfaces and their discretizations without
any underlying specific parametrization. They can also model more specific
geometrical objects such as bundles of curves, vector or tensor fields,
functions defined on surfaces, etc.
Next, by defining Hilbert dual norms on spaces of currents, we get a
practicable notion of closeness between geometrical objects, and it becomes
possible to apply some statistical methods in this framework. We will see
the example of template estimation, which is of great interest for the
analysis of medical images: how to estimate a geometrical template out
of a collection of observed objects which are modelled as noisy
deformations of the template. I will present different methods
to answer this problem in the framework of currents; and I will show
several applications, mainly in the field of brain imaging.
- Yann Gousseau, Telecom ParisTech
Title: ``Germ-grain models and image synthesis''
Germ-grain models enable the modeling of images by means of random shapes. In
this talk, I will present several models in which shapes interact in various
ways : addition (shot noise), union (boolean models), occlusion (dead leaves)
or transparency. Several applications of these models will be presented, with
an emphasis on texture synthesis. Then, I will show how variants of
germ-grain models enable the synthesis of complex abstract images, the
reproduction of stroke-based vector textures as well as the creation of image
- Rafael Grompone,
École Normale Supérieure de Cachan
Title: ``Towards Partial Gestalt Fusion''
In this talk I will present preliminary results of ongoing
work toward a mathematical and computational
formalization of Gestalt grouping laws. We did not try to
formalize the Gestalt laws in exactly the same terms as
they were expressed. Instead, we formalized some simple
geometric structures that we hope include the same
concepts. The structures currently analyzed are line
segment chains, alignments of points and line segments,
and a restricted form of parallelism that we called strokes.
To evaluate the relevance of the results, we developed a
perceptual test that we called the Nachtanz, that allows
to compare the analysis done with these tools to human
- Laurent Itti, University of Southern California, USA
Title: ``Statistical modeling of surprise with applications to images and videos''
The amount of information contained in a piece of data can be measured
by the effect this data has on its observer. Fundamentally, this
effect is to transform the observer's prior beliefs into posterior
beliefs, according to Bayes theorem. Thus the amount of information
can be measured in a natural way by the distance (relative entropy)
between the prior and posterior distributions of the observer over the
available space of hypotheses. This facet of information, termed
``surprise'', is important in dynamic situations where beliefs change,
in particular during learning and adaptation. Surprise can often be
computed analytically, for instance in the case of distributions from
the exponential family, or it can be numerically approximated. During
sequential Bayesian learning, surprise decreases like the inverse of
the number of training examples. Theoretical properties of surprise
are discussed, in particular how it differs and complements Shannon's
definition of information. A computer vision neural network
architecture is then presented capable of computing surprise over
images and video stimuli. Hypothesizing that surprising data ought to
attract natural or artificial attention systems, the output of this
architecture is used in a psychophysical experiment to analyze human
eye movements in the presence of natural video stimuli. Surprise is
found to yield robust performance at predicting human gaze (ROC-like
ordinal dominance score ~0.7 compared to ~0.8 for human
inter-observer repeatability, ~0.6 for simpler intensity
contrast-based predictor, and 0.5 for chance). The resulting theory
of surprise is applicable across different spatio-temporal scales,
modalities, and levels of abstraction.
Joint work with Prof. Pierre Baldi, University of California Irvine.
- Jérémie Jakubowicz, Telecom ParisTech
Title: ``Low-resolution aircraft detection using level set statistics''
In this talk we will show that level sets are well suited to detect aircraft
on low resolution infrared images. Aircraft correspond to hot temperatures
at the sensor level. Hence it is natural to rely on a test that considers the
hottest pixels in the sensed image. If these pixels are close, they are likely
to come from a target; otherwise they should belong to the clutter. Instead of
proposing an ad hoc procedure to test the neighborhood of each hot pixel, we
rely on level sets. However, calibrating the test raise some questions about
the level sets statistical properties under a random fields models. We will
recall some known facts from the statistical level lines geometry theory mainly
from the work of Adler & Taylor and Azais & Wschebor. After what we will
conclude with some open questions.
Adler, R. J. and Taylor, J. E., "Random Fields And Their Geometry",
Azais, J.-M. and Wschebor, M., "Level Sets and Extrema of Random
Processes and Fields", Wiley-Blackwell, 2009.
- Ann Lee, Carnegie Mellon University, USA
Title: ``Spectral Connectivity Analysis with an Application to Image Retrieval and Texture Discrimination''
For natural images, the dimension of the given input space is
often very large while the data themselves have a low intrinsic
dimensionality. Spectral kernel methods are non-linear techniques for
transforming data into a coordinate system that efficiently reveals the
geometry of the underlying distribution. In this talk, we describe
``diffusion maps''; the construction is based on a Markov random walk on
the data and offers a general scheme of simultaneously reorganizing and
quantizing arbitrarily shaped data sets in high dimensions using
intrinsic geometry. We present a novel extension of the diffusion
framework to comparing distributions in high-dimensional feature spaces
with an application to image retrieval and texture discrimination.
- Erwan Le Pennec, Université Paris 7
Title: ``An aggregated point of view on NL-Means''
Patch based estimators, examplified by the Non Local Means, give results
close to the state of the art despite their conceptual simplicity.
Tackling their mathematical properties is still a challenge.
In this work with J. Salmon, we propose an approach based on
PAC-Bayesian aggregation techniques. The estimators obtained are, as in
the NL Mean case, local weighted average of patches. The weights are
different and allow to obtain some control on the peformance of these
I would like to present the corresponding theoretical framework and
explain how to deduce some estimators efficient theoretically and
- Nicolas Lermé, Université Paris 13
Title: ``Reducing graphs for graph cut segmentation''
In few years, graph cuts have become a leading method for solving a
wide range of problems in computer vision and graphics. However, graph cuts
involve the construction of huge graphs which sometimes do not fit in memory.
Currently, most of the max-flow algorithms are totally impracticable to solve
such large scale problems. In the image segmentation context, some
proposed banded of hierarchical approximation methods to get round this
We propose a new strategy for reducing graphs during the creation of the graph
where the nodes of the reduced graph are typically located in a narrow band
surrounding the object edges. Empirically, solutions obtained on the reduced
graphs are identical to the solutions on the complete graphs. Moreover, the
time required by the reduction if often compensated by the time that would be
needed to create the remove nodes and the additional time required by the
max-flow on the larger graph. Finally, we show experiments for
segmenting large volume data in 2D and 3D.
Keywords: segmentation, graph cut, reduction.
- Cécile Louchet, Université d'Orléans
Title: ``Total Variation as a local filter''
In the Rudin-Osher-Fatemi (ROF) image denoising model, Total Variation (TV)
is used as a global regularization term. However, as we observe, the local
interactions induced by Total Variation do not propagate much at long
in practice, so that the ROF model is not far from being a local filter. In
this talk, we propose to build a purely local filter by considering the ROF
model in a given neighborhood of each pixel. We study theoretical
the obtained local filter, and show that it brings an interesting optimization
of the bias-variance trade-off, and a strong reduction a ROF drawback called
"staircasing effect". We finally present a new denoising algorithm, TV-means,
that efficiently combines the idea of local TV-filtering with the non-local
means patch-based method.
- Julien Mairal, INRIA
Title: ``Non-local sparse models for image restoration''
We propose to unify two different approaches to image restoration: On
the one hand, learning a basis set (dictionary) adapted to sparse signal
descriptions has proven to be very effective in image reconstruction and
classification tasks. On the other hand, explicitly exploiting the
self-similarities of natural images has led to the successful non-local
means approach to image restoration. We propose simultaneous sparse
coding as a framework for combining these two approaches in a natural
manner. This is achieved by jointly decomposing groups of similar
signals on subsets of the learned dictionary. Experimental results in
image denoising and demosaicking tasks with synthetic and real noise
show that the proposed method outperforms the state of the art, making
it possible to effectively restore raw images from digital cameras at a
reasonable speed and memory cost.
- Éric Moulines, Telecom ParisTech
Title: ``Adaptive and interacting MCMC algorithms''
We review adaptive Markov chain Monte Carlo algorithms (MCMC) as a
mean to optimise their mixing property.
Using simple examples, we introduce a general theoretical framework,
covering a large number of adaptation algorithms, including both
"internal" and "external" adaptations and the case where the parameter
to adapt is infinite dimensional (the so-called self-interacting MCMC).
This theory leads to guidelines concerning the design of proper
We then review criteria and propose methods which allows one to
systematically optimize generally used criteria, but also analyze the
properties of the resulting adaptive MCMC algorithms. We then propose
a series of novel adaptive algorithms which prove to be robust and
reliable in practice. The behavior of these algorithms is illustrated
on some challenging simulation problems.
- Gabriel Peyré, Université Paris-Dauphine and CNRS
Title: ``A review of statistical texture synthesis methods, with a new one''
In this talk, I will survey statistical approaches to the problem of natural texture synthesis.
I will focuss in particular on simple statistical constraints over transformed domains, such as oriented
filter banks or wavelets. These constraints can be enforced using histogram equalization. This allows to model
complicated joint densities using optimal transport algorithms, which offers a conventien unifying framework for
color texture modeling. In a joint work with Julien Rabin, we are using this formalism to perform texture mixing, which
allows to perform arbitrary convex combination of textures using barycentric coordinates using the Wasserstein optimal
Many of the proposed models can be "tested" though a series of Matlab/Scilab experiments,
the "Numerical Tours of Signal Processing", section "Computer Graphics".
- Brian Potetz, University of Kansas, USA
Title: ``Causes & Consequences of the Nearness/Brightness Correlation in Natural Images''
Since Leonardo da Vinci, artists and psychologists have known
that, all other things being equal, brighter stimuli are perceived to be
closer than darker stimuli. Using a laser range scanner, we have shown
that this perceptual bias is adaptive: in natural scenes, brighter stimuli
are in fact more likely to be closer. I present evidence that this
statistical tendency is caused by shadows in complex natural scenes, and
discuss the implications of this with respect to previous explanations
offered for the psychophysical phenomenon. Next, we show the potential
that this cue has in computer vision, and demonstrate that in natural
scenes, the strength of this cue can rival that of shading. Finally, we
present single-cell recording data from awake behaving macaques that shows
that this statistical trend is exploited in the visual system as early as
V1. Specifically, cells that prefer near disparities tend to prefer
- Maël Primet, Université Paris Descartes
Title: ``Trajectory detection in the a-contrario framework''
Given a sequence of point sets, can we detect smooth
trajectories that may be partially occluded and mixed to noise points?
Using the a-contrario framework and dynamic programming, we obtain a
algorithm that performs an exhaustive search of meaningful trajectories
while controlling the number of (false) detections in pure noise
sequences. The a-contrario framework also provides a simple perceptual
criterion for trajectory appearances, which enables us
to gain a theoretical insight on perceptual limits for the trajectory
detection problem. We show the benefits and drawbacks of this approach,
and compare it to state-of-the-art point tracking algorithms.
- Frédéric Richard, Université Paris Descartes
Title: ``Statistical analysis of anisotropic Brownian image textures''
In this talk, I focus on the analysis of anisotropy in image textures. To deal mathematically with this issue, I present a statistical framework gathering some anisotropic extensions of the fractional Brownian field. In this framework, I give several asymptotic results about the estimation of model parameters and the testing of anisotropy. I also present some applications to bone X-ray images and mammograms.
- François Roueff, Telecom ParisTech
Title: ``Weak convergence of a regularization path''
Consider an estimator defined as the minimizer of a goodness-of-fit measure
including a penalty. The regularization path, sometimes also called the
solution path, is defined as the curve described by the estimator as the
penalty weight varies from zero to infinity. The higher the penalty weight, the
more regular the estimator in a sense depending on the penalty choice. A
quadratic penalty is often used for ill-posed inverse problems and amounts to
look for solutions within an Euclidean ball. On the other hand, an absolute
deviation penalty is used to impose sparse solutions. Since the choice of the
penalty weight is a difficult issue, the practitionner is often interested in
the whole or a part of the regularization path. It is thus important to
understand how this path behaves, at least asymptotically for a large number of
observations. The goal of this talk is to explain how, under fairly general
conditions, this behavior is depicted by a non-central limit theorem of the
regularization path, conveniently centered and normalized.
- Neus Sabater, École Normale Supérieure de Cachan
Title: ``Optimal Stereo Matching
Reaches Theoretical Accuracy Bounds''
3D reconstruction from two images requires the perfect control of a long
chain of algorithms: internal and external calibration,
stereo-rectification, block-matching, and 3D reconstruction. This work
focuses on the crucial block-matching step. This work provides an exact
mathematical formula to estimate the disparity error caused by noise.
Then, this exact estimate is confirmed by a new implementation of block
matching eliminating most false alarms, where the residual errors are
therefore mainly due to the noise. Based on several examples we have
shown that in a completely realistic setting 40% to 90% of pixels of an
image could be matched with an accuracy of about 5/100 pixels. Moreover,
the predicted theoretical error due to noise is nearly equal to the
error achieved by our algorithm on simulated and real images pairs.
- Hichem Sahbi, Telecom ParisTech
``Context-Dependent Image Matching, Recognition and Retrieval''
Initially motivated by the success of closely related areas,
"context-dependent" scene recognition and retrieval techniques are
currently emerging; their general principle consists of modeling the
visual appearance of objects into scenes as well as their dependencies.
In this talk, I will focus on kernel machines for object/scene
recognition and retrieval using a new class of kernels, referred to as
"context-dependent" (CDKs). This class is defined as the fixed point of
an energy function mixing (1) a fidelity term which measures the
intrinsic visual similarity between images (2) a neighborhood criterion
that captures image geometry/context and (3) a regularization term. I
will also discuss some theoretical issues about the convergence of CDKs
and their positive definiteness so they can be used in many kernel
methods including support vector machines. Finally, I will illustrate
some results mainly in object recognition, network-dependent image
search, pattern matching and detection.
- Joseph Salmon, Université Paris Diderot
Title: ``Reprojections for Non-Local Means''
Since their introduction in denoising, the family of non-local
methods, whose Non-Local Means (NL-Means) is the most famous member,
has proved its ability to challenge other powerful methods like wavelets
or variational techniques. Though simple to implement and efficient in
practice, the classical NL-Means algorithm suffers from several
limitations: ringing artifacts are created around edges and regions with
few repetitions in the image are not treated at all. We present an easy
to implement and time efficient modification of the NL-means based on a
better reprojection from the patches space to the original pixels space,
specially designed to reduce those artifacts.
Authors: J. Salmon , Y.Strozecki.
- Alain Trouvé, École Normale Supérieure de Cachan
Title: ``The more you look, the more you see:
Efficient resource allocation
for optimal speed curve detection and identification in noise images''
Despite both problems of statistical detectability on one
hand and deterministic algorithm complexity on the other hand are
quite well studied, the problem of understanding the fundamental
statistical and computational limits of object detection and recognition
algorithms are much less explored. We propose in this talk to have a
closer look at this problem in the toy but still theoretically and practically
challenging situation of curve detection and identification in noise images.
On going work with Yali Amit.
- Tieyong Zeng, Hong Kong Baptist University, China
Title: ``Poisson noise removal''
In this talk, we address a new variational approach for Poisson noise removal problem.
The new proposed model contains three terms: one is from the sparse representation of the transformed image via VST; one is a data-fidelity term caused by the statistical properties of Poisson noise, and a Total Variation regularization (TV) in the transformed image domain. Comparative experiments for gray images are carried out to show the leading performance of our new model.