Video object watermarking robust to manipulations

This paper presents a watermarking scheme that embeds a signature in video objects for the MPEG-4 video standard. The different constraints associated with this standard are quite different from classical video watermarking schemes. The mark detection had to be achieved after different video object manipulations such as rotation or scaling operations. Principal component analysis and warping methods are used to enable the synchronization of the mark after geometric manipulations. The embedding of the mark is done adding an oriented random sequence and the detection of the mark is processed using a correlation criterion. The different results point out the fact that the presented scheme can detect the mark after bit-rate modification, object shape sub-sampling and geometric manipulations (scaling and rotations).Cet article présente un schéma de tatouage permettant de marquer des objets vidéo tels qu'ils sont décrits dans le cadre de la norme MPEG-4. Les contraintes liées à cette norme sont différentes de celles connues en tatouage de séquences classiques. Dans un tel contexte, la détection de la signature doit en effet être possible après diverses manipulations de l'objet vidéo telles que des rotations ou changements d'échelle. La méthode proposée utilise la forme de l'objet vidéo pour permettre la synchronisation de la signature. Cette étape est effectuée en utilisant des techniques d'analyse en composantes principales et de « morphing » de séquences de forme prédéfinie. L'insertion de la signature s'effectue ensuite par addition d'une séquence aléatoire orientée, et la détection s'opère par corrélation. Les tests appliqués sur des objets vidéo indiquent que le schéma présenté permet la détection de la signature après des opérations telles que la réduction du débit, le sous-échantillonnage du masque associé à l'objet, ou encore des manipulations géométriques (rotations, changements d'échelle)

Prognostic Power of Texture Based Morphological Operations in a Radiomics Study for Lung Cancer

The importance of radiomics features for predicting patient outcome is now well-established. Early study of prognostic features can lead to a more efficient treatment personalisation. For this reason new radiomics features obtained through mathematical morphology-based operations are proposed. Their study is conducted on an open database of patients suffering from Nonsmall Cells Lung Carcinoma (NSCLC). The tumor features are extracted from the CT images and analyzed via PCA and a Kaplan-Meier survival analysis in order to select the most relevant ones. Among the 1,589 studied features, 32 are found relevant to predict patient survival: 27 classical radiomics features and five MM features (including both granularity and morphological covariance features). These features will contribute towards the prognostic models, and eventually to clinical decision making and the course of treatment for patients.Comment: 9 pages, 3 tables, 3 figures, 31 reference

The Agile UX Development Lifecycle: Combining Formative Usability and Agile Methods

This paper contributes a method variation that helps cross-functional teams combine both formative usability and agile methods to develop interactive systems. Both methods are iterative, continuous and focus on delivering value to users, which makes their combination possible. The “agile UX development lifecycle” supports and facilitates the synchronization of the steps involved in both formative usability and agile sprints in an operable manner and is intended for design and development settings. We present a case study that illustrates the extent to which this tool meets the needs of real-world cross-functional teams, describing the gains in efficiency it can provide but also guidelines for increasing the benefits gained from this combination in design and development settings

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

<p>Abstract</p> <p>Background</p> <p>Falls 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.</p> <p>Method</p> <p>A 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.</p> <p>Results</p> <p>The 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.</p> <p>Conclusions</p> <p>This 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.</p

Détection axiale de turbulences de sillage par LIDAR Doppler dans un volume fluide simulé et reconstruction d'images 2,5 D

Les turbulences de sillage, causées par le déplacement dans l'atmosphère des ailes d'un avion, sont potentiellement extrêmement dangereuses pour un avion suivant l'avion générateur. Celles-ci peuvent être détectées par LIDAR Doppler; toutefois, l'utilisation (par exemple dans le cas d'un senseur embarqué) d'une configuration d'analyse coaxiale aux vortex rend cette détection plus délicate. Cet article présente une méthode de génération d'images 2,5 D par triangulation de Delaunay à partir d'une simulation simplifiée de tirs LIDAR Doppler balayant suivant un motif sinusoïdal bruité des turbulences de sillage simulées. Nous validons ainsi la viabilité d'un balayage axial des turbulences de sillage et par suite la faisabilité d'un dispositif de détection et d'avertissement embarqué dans un avion

Diffeomorphic Registration of Images with Variable Contrast Enhancement

Nonrigid image registration is widely used to estimate tissue deformations in highly deformable anatomies. Among the existing methods, nonparametric registration algorithms such as optical flow, or Demons, usually have the advantage of being fast and easy to use. Recently, a diffeomorphic version of the Demons algorithm was proposed. This provides the advantage of producing invertible displacement fields, which is a necessary condition for these to be physical. However, such methods are based on the matching of intensities and are not suitable for registering images with different contrast enhancement. In such cases, a registration method based on the local phase like the Morphons has to be used. In this paper, a diffeomorphic version of the Morphons registration method is proposed and compared to conventional Morphons, Demons, and diffeomorphic Demons. The method is validated in the context of radiotherapy for lung cancer patients on several 4D respiratory-correlated CT scans of the thorax with and without variable contrast enhancement