Benoît Macq

Geometrically invariant watermarking using feature points

This paper presents a new approach for watermarking of digital images providing robustness to geometrical distortions. The weaknesses of classical watermarking methods to geometrical distortions are outlined first. Geometrical distortions can be decomposed into two classes: global transformations such as rotations and translations and local transformations such as the StirMark attack. An overview of existing self-synchronizing schemes is then presented. Theses schemes can use periodical properties of the mark, invariant properties of transforms, template insertion, or information provided by the original image to counter geometrical distortions. Thereafter, a new class of watermarking schemes using the image content is presented. We propose an embedding and detection scheme where the mark is bound with a content descriptor defined by salient points. Three different types of feature points are studied and their robustness to geometrical transformations is evaluated to develop an enhanced detector. The embedding of the signature is done by extracting feature points of the image and performing a Delaunay tessellation on the set of points. The mark is embedded using a classical additive scheme inside each triangle of the tessellation. The detection is done using correlation properties on the different triangles. The performance of the presented scheme is evaluated after JPEG compression, geometrical attack and transformations. Results show that the fact that the scheme is robust to these different manipulations. Finally, in our concluding remarks, we analyze the different perspectives of such content-based watermarking scheme.

Circular interpretation of bijective transformations in lossless watermarking for media asset management

The need for reversible or lossless watermarking methods has been highlighted in the literature to associate subliminal management information with losslessly processed media and to enable their authentication. The paper first analyzes the specificity and the application scope of lossless watermarking methods. It explains why early attempts to achieve reversibility are not satisfactory. They are restricted to well-chosen images, strictly lossless context and/or suffer from annoying visual artifacts. Circular interpretation of bijective transformations is proposed to implement a method that fulfills all quality and functionality requirements of lossless watermarking. Results of several bench tests demonstrate the validity of the approach.

Registration of 3-d intraoperative MR images of the brain using a finite-element biomechanical model

We present a new algorithm for the nonrigid registration of three-dimensional magnetic resonance (MR) intraoperative image sequences showing brain shift. The algorithm tracks key surfaces of objects (cortical surface and the lateral ventricles) in the image sequence using a deformable surface matching algorithm. The volumetric deformation field of the objects is then inferred from the displacements at the boundary surfaces using a linear elastic biomechanical finite-element model. Two experiments on synthetic image sequences are presented, as well as an initial experiment on intraoperative MR images showing brain shift. The results of the registration algorithm show a good correlation of the internal brain structures after deformation, and a good capability of measuring surface as well as subsurface shift. We measured distances between landmarks in the deformed initial image and the corresponding landmarks in the target scan. Cortical surface shifts of up to 10 mm and subsurface shifts of up to 6 mm were recovered with an accuracy of 1 mm or less and 3 mm or less respectively.

Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry

BackgroundFalls in the elderly is nowadays a major concern because of their consequences on elderly general health and moral states. Moreover, the aging of the population and the increasing life expectancy make the prediction of falls more and more important. The analysis presented in this article makes a first step in this direction providing a way to analyze gait and classify hospitalized elderly fallers and non-faller. This tool, based on an accelerometer network and signal processing, gives objective informations about the gait and does not need any special gait laboratory as optical analysis do. The tool is also simple to use by a non expert and can therefore be widely used on a large set of patients.MethodA population of 20 hospitalized elderlies was asked to execute several classical clinical tests evaluating their risk of falling. They were also asked if they experienced any fall in the last 12 months. The accelerations of the limbs were recorded during the clinical tests with an accelerometer network distributed on the body. A total of 67 features were extracted from the accelerometric signal recorded during a simple 25 m walking test at comfort speed. A feature selection algorithm was used to select those able to classify subjects at risk and not at risk for several classification algorithms types.ResultsThe results showed that several classification algorithms were able to discriminate people from the two groups of interest: fallers and non-fallers hospitalized elderlies. The classification performances of the used algorithms were compared. Moreover a subset of the 67 features was considered to be significantly different between the two groups using a t-test.ConclusionsThis study gives a method to classify a population of hospitalized elderlies in two groups: at risk of falling or not at risk based on accelerometric data. This is a first step to design a risk of falling assessment system that could be used to provide the right treatment as soon as possible before the fall and its consequences. This tool could also be used to evaluate the risk several times during the revalidation procedure.

Serial registration of intraoperative MR images of the brain.

The increased use of image-guided surgery systems during neurosurgery has brought to prominence the inaccuracies of conventional intraoperative navigation systems caused by shape changes such as those due to brain shift. We propose a method to track the deformation of the brain and update preoperative images using intraoperative MR images acquired at different crucial time points during surgery. We use a deformable surface matching algorithm to capture the deformation of boundaries of key structures (cortical surface, ventricles and tumor) throughout the neurosurgical procedure, and a linear finite element elastic model to infer a volumetric deformation. The boundary data are extracted from intraoperative MR images using a real-time intraoperative segmentation algorithm. The algorithm has been applied to a sequence of intraoperative MR images of the brain exhibiting brain shift and tumor resection. Our results characterize the brain shift after opening of the dura and at the different stages of tumor resection, and brain swelling afterwards. Analysis of the average deformation capture was assessed by comparing landmarks identified manually and the results indicate an accuracy of 0.7+/-0.6 mm (mean+/-S.D.) for boundary surface landmarks, of 0.9+/-0.6 mm for landmarks inside the boundary surfaces, and 1.6+/-0.9 mm for landmarks in the vicinity of the tumor.

Watermarking algorithm based on a human visual model

This paper presents an additive watermarking technique for grey-scale pictures. It consists in secretly embedding copyright information (a binary code) into the picture without degrading its quality. Those bits are encoded through the phase of maximal length sequences (MLS). MLS are binary sequences with good correlation properties. The result of the autocorrelation is much greater than crosscorrelations, i.e; correlations made with shifted versions of this sequence. The embedded bits are retrieved from the result of the correlations. The core of the embedding process is underlaid by a masking criterion that guarantees the invisibility of the watermark. It is combined with an edge and texture discrimination to determine the embedding level of the MLS, whose bits are actually spread over 32 x 8 pixel blocks. Eventually, some results are presented, which analyze the efficiency of the retrieval as well as the resistance of the watermark to compression and its robustness against malevolent manipulation

The eNTERFACE’05 Audio-Visual Emotion Database

This paper presents an audio-visual emotion database that can be used as a reference database for testing and evaluating video, audio or joint audio-visual emotion recognition algorithms. Additional uses may include the evaluation of algorithms performing other multimodal signal processing tasks, such as multimodal person identification or audio-visual speech recognition. This paper presents the difficulties involved in the construction of such a multimodal emotion database and the different protocols that have been used to cope with these difficulties. It describes the experimental setup used for the experiments and includes a section related to the segmentation and selection of the video samples, in such a way that the database contains only video sequences carrying the desired affective information. This database is made publicly available for scientific research purposes.

Signal-adapted multiresolution transform for image coding

The authors consider the problem of designing multiresolution transforms that are adapted to the given image signal, in the sense that they maximize the coding gain at each resolution level. A simple alternating optimization algorithm is derived for solving this problem in the framework of the lattice realization of para-unitary quadrature mirror filters. The resulting large coding scheme is discussed in some detail, and its performance is compared with that of the discrete cosine transform (JPEG) technique and with that of some nonadapted multiresolution transforms. >

Data hiding on 3-D triangle meshes

In this paper, we present a new scheme for digital steganography of three-dimensional (3-D) triangle meshes. This scheme is robust against translation, rotation, and scaling operations. It is based on a substitutive procedure in the spatial domain. The key idea is to consider a triangle as a two-state geometrical object. We discuss its performance in terms of capacity, complexity, visibility, and security. We validate the use of a principal component analysis (PCA) to make our scheme signal-dependent in the line of second generation watermarking scheme. We also define a simple specific metric for distortion evaluation that has been validated by many tests. We conclude by giving some other solutions, including open steganographic schemes that could be derived from the basic ideas presented here.

Invisibility and application functionalities in perceptual watermarking an overview

Digital watermarking consists of hiding subliminal information into digital media content, also called host data. It can be the basis of many applications, including security and media asset management. In this paper we focus on the imperceptibility requirement for image watermarking. We first provide a functional inventory of image watermarking applications and emphasize the dependency between the application purpose and its need for invisibility. Then, we present a global framework common to most existing watermarking systems. It illustrates the methodology followed to translate human vision research into watermarking technology. It suggests future prospects and highlights the need for dedicated inputs from the human vision community.