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International audience; In this paper, we present a medical image integrity verification system to detect and approximate local malevolent image alterations (e.g. removal or addition of lesions) as well as identifying the nature of a global processing an image may have undergone (e.g. lossy compression, filtering ...). The proposed integrity analysis process is based on non significant region watermarking with signatures extracted from different pixel blocks of interest and which are compared with the recomputed ones at the verification stage. A set of three signatures is proposed. The two firsts devoted to detection and modification location are cryptographic hashes and checksums, while the last one is issued from the image moment theory. In this paper, we first show how geometric moments can be used to approximate any local modification by its nearest generalized 2D Gaussian. We then demonstrate how ratios between original and recomputed geometric moments can be used as image features in a classifier based strategy in order to determine the nature of a global image processing. Experimental results considering both local and global modifications in MRI and retina images illustrate the overall performances of our approach. With a pixel block signature of about 200 bit long, it is possible to detect, to roughly localize and to get an idea about the image tamper.

In this paper, the duality in differential form is developed between a 3D primal surface and its dual manifold formed by the surface's tangent planes, that is, each tangent plane of the primal surface is represented as a four-dimensional vector that constitutes a point on the dual manifold. The iterated dual theorem shows that each tangent plane of the dual manifold corresponds to a point on the original 3D surface, that is, the "dual" of the "dual" goes back to the "primal." This theorem can be directly used to reconstruct 3D surface from image edges by estimating the dual manifold from these edges. In this paper, we further develop the work in our original conference papers resulting in the robust differential dual operator. We argue that the operator makes good use of the information available in the image data by using both points of intensity discontinuity and their edge directions; we provide a simple physical interpretation of what the abstract algorithm is actually estimating and why it makes sense in terms of estimation accuracy; our algorithm operates on all edges in the images, including silhouette edges, self occlusion edges, and texture edges, without distinguishing their types (thus, resulting in improved accuracy and handling locally concave surface estimation if texture edges are present); the algorithm automatically handles various degeneracies; and the algorithm incorporates new methodologies for implementing the required operations such as appropriately relating edges in pairs of images, evaluating and using the algorithm's sensitivity to noise to determine the accuracy of an estimated 3D point. Experiments with both synthetic and real images demonstrate that the operator is accurate, robust to degeneracies and noise, and general for reconstructing free-form objects from occluding edges and texture edges detected in calibrated images or video sequences.

Novelty-based segmentation of audio signals has proven good performances for the estimation of boundaries of structural sections within music pieces. However, boundaries are detected only if structural sections satisfy the condition of sufficient acoustic inner-homogeneity. While this constraint is very restrictive and not representative of all musical contents, we propose in this paper to extend the detection of acoustic novelty to transitions between homogeneous and non-homogeneous sections and vice versa. Moreover, the length of the considered sections for the boundary detection is crucial, we also introduce a multi-scale novelty approach that allows to capture boundaries between sections of different temporal scales in a same segmentation. Evaluation of the combination of these two methods proves convincing results for temporal segmentation of music pieces. Embedding the algorithm in a music structure segmentation system, we show that performances can be consistently improved for this task.

cote interne IRCAM: Lanchantin11a; None / None; National audience; Hidden Markov chains (HMC) are a very powerful tool in hidden data restoration and are currently used to solve a wide range of problems. However, when these data are not stationary, estimating the parameters, which are required for unsupervised processing, poses a problem. Moreover, taking into account correlated non-Gaussian noise is difficult without model approximations. The aim of this paper is to propose a simultaneous solution to both of these problems using triplet Markov chains (TMC) and copulas. The interest of the proposed models and related processing is validated by different experiments some of which are related to semi-supervised and unsupervised image segmentation.

This article deals with logical and graphical representations of uncertain information in a quantitative possibilistic theory framework. We first provide a deep analysis of relationships between these two forms of representational frameworks. Then, we provide a novel algorithm for reasoning with quantitative possibilistic logic. The algorithm exploits the syntactic relations between quantitative possibilistic logic bases and penalty logic bases. We provide experimental results that compare the propagation algorithm developed for the possibilistic product-based networks and the inference algorithm developed in this paper. quantitative possibilistic theory, penalty logic, uncertainty propagation algorithm.

cote interne IRCAM: Lanchantin08b; None / None; National audience; The hidden Markov chain (HMC) model is a couple of random sequences (X,Y), in which X is an unobservable Markov chain, and Y is its observable noisy version. Classically, the distribution p(y|x) is simple enough to ensure the Markovianity of p(x|y), that enables one to use different Bayesian restoration techniques. HMC model has recently been extended to "pairwise Markov chain" (PMC) model, in which one directly assumes the Markovianity of the pair Z=(X,Y), and which still enables one to recover X from Y. Finally, PMC has been extended to "triplet Markov chain" (TMC) model, which is obtained by adding a third chain U and considering the Markovianity of the triplet T=(X,U,Y). When U is not too complex, X can still be recovered from Y. Then U can model different situations, like non-stationarity or semi-Markovianity of (X,Y). Otherwise, PMC and TMC have been extended to pairwise "partially" Markov chains (PPMC) and triplet "partially" Markov chains (TPMC), respectively. In a PPMC Z=(X,Y) the distribution p(x|y) is a Markov distribution, but p(y|x) may not be and, similarly, in a TPMC T=(X,U,Y) the distribution p(x,u|y) is a Markov distribution, but p(y|x,u) may not be. However, both PPMC and TPMC can enable one to recover X from Y, and TPMC include different long-memory noises. The aim of this paper is to show how a particular Gaussian TPMC can be used to segment a discrete signal hidden with long-memory noise. An original parameter estimation method, based on "Iterative Conditional Estimation" (ICE) principle, is proposed and some experiments concerned with unsupervised segmentation are provided. The particular unsupervised segmentation method used in experiments can also be seen as identification of different stationarities in fractional Brownian noise, which is widely used in different problems in telecommunications, economics, finance, or hydrology.

Content based image retrieval using spatial image content (i.e. using multiple regions and their spatial relationships) is still an open problem which has received considerable attention in literature. In this paper we introduce a new representation of image based on the most general spatial image content representation that is Attributed Relation Graphs (ARG) representation and also a new method of image indexation. Like all CBIR systems, the one proposed here has two components: a segmentation component and a matching component using a novel inexact graph matching algorithm. We tested our work in lettrines image but it's also valid in general image.

In this paper, a graph classification approach based on a multi-objective genetic algorithm is presented. The method consists in the learning of sets composed of synthetic graph prototypes which are used for a classification step. These learning graphs are generated by simultaneously maximizing the recognition rate while minimizing the confusion rate. Using such an approach the algorithm provides a range of solutions, the couples (confusion, recognition) which suit to the needs of the system. Experiments are performed on real data sets, representing 10 symbols. These tests demonstrate the interest to produce prototypes instead of finding representatives which simply belong to the data set.

Periodicity estimation of an audio signal, for applications such as pitch, multiple pitch or tempo estimation is often problematic due to the presence of multiple harmonics in the audio signal producing octave errors. While pitch models or rhythm models can be used, they remain often dedicated to a specific problem. In this paper, we propose a straightforward approach for periodicity estimation based on the combination of a spectral representation and a temporal representation. This method allows a better emphasis on the frequencies corresponding to the various pitches. We show the ability of this representation to adequately estimate pitch and visualize signals with multiple pitch content.

International audience; This paper describes an original approach for content-based video indexing and retrieval. We aim at providing a global interpretation of the dynamic content of video shots without any prior motion segmentation and without any use of dense optic flow fields. To this end, we exploit the spatio-temporal distribution, within a shot, of appropriate local motion-related measurements derived from the spatio-temporal derivatives of the intensity function. These distributions are then represented by causal Gibbs models. To be independent of camera movement, the motion-related measurements are computed in the image sequence generated by compensating the estimated dominant image motion in the original sequence. The statistical modeling framework considered makes the exact computation of the conditional likelihood of a video shot belonging to a given motion or more generally to an activity class feasible. This property allows us to develop a general statistical framework for video indexing and retrieval with query-by-example. We build a hierarchical structure of the processed video database according to motion content similarity. This results in a binary tree where each node is associated to an estimated causal Gibbs model. We consider a similarity measure inspired from Kullback-Leibler divergence. Then, retrieval with query-by-example is performed through this binary tree using the maximum a posteriori (MAP) criterion. We have obtained promising results on a set of various real image sequences