Geogenic and atmospheric sources for volatile organic compounds in fumarolic emissions from Mt. Etna and Vulcano Island (Sicily, Italy)

In this paper, fluid source(s) and processes controlling the chemical composition of volatile organic compounds (VOCs) in gas discharges from Mt. Etna and Vulcano Island(Sicily, Italy) were investigated. The main composition of the Etnean and Volcano gas emissions is produced by mixing, to various degrees, of magmatic and hydrothermal components. VOCs are dominated by alkanes, alkenes and aromatics, with minor, though significant, concentrations of O-, S- and Cl(F)-substituted compounds. The main mechanism for the production of alkanes is likely related to pyrolysis of organic-matterbearing sediments that interact with the ascending magmatic fluids. Alkanes are then converted to alkene and aromatic compounds via catalytic reactions (dehydrogenation and dehydroaromatization, respectively). Nevertheless, an abiogenic origin for the light hydrocarbons cannot be ruled out. Oxidative processes of hydrocarbons at relatively high temperatures and oxidizing conditions, typical of these volcanic-hydrothermal fluids, may explain the production of alcohols, esters, aldehydes, as well as O- and S-bearing heterocycles. By comparing the concentrations of hydrochlorofluorocarbons (HCFCs) in the fumarolic discharges with respect to those of background air, it is possible to highlight that they have a geogenic origin likely due to halogenation of both methane and alkenes. Finally, chlorofluorocarbon (CFC) abundances appear to be consistent with background air, although the strong air contamination that affects the Mt. Etna fumaroles may mask a possible geogenic contribution for these compounds. On the other hand, no CFCs were detected in the Vulcano gases, which are characterized by low air contribution. Nevertheless, a geogenic source for these compounds cannot be excluded on the basis of the present data

Visual saliency detection in colour images based on density estimation

International audienceA simple and effective method for visual saliency detection in colour images is presented. The method is based on the common observation that local salient regions exhibit distinct geometric and and texture patterns from neighbouring regions. We model the colour distribution of local image patches with a Gaussian density and measure the saliency of each patch as the statistical distance from that density. Experimental results with public datasets and comparison with other state-of-the-art methods show the effectiveness of our method

Physical Filtering of Polarization-Encoded Images by Peano-Hilbert Fractal Path

In the framework of Stokes parameters imaging, polarization-encoded images have four channels. The relevant of such multidimensional structure comes from the set of physical information they carry about the local nature of the target. However, the noise that affects the intensity measurement may induce the no physical character of Stokes parameters and make awkward their analysis and processing. In this paper a new method to filter the additive noise of polarimetric measurement is introduced. This method is based on two multispectral filtering methods combined with a transformation of the Stokes channels following a fractal path. Using a regularization parameter, the proposed algorithm is a tradeoff between the filtering of polarization-encoded images and the preserving of their physical content. The statistical performances of the method are tested on simulated and real images using Bootstrap re-sampling.L’existence du bruit est inhérente aux systèmes imageurs. Causé par différents mécanismes, sa présence dégrade en général l’interprétation des données et peut dans le cas de l’imagerie polarimétrique nuire au caractère physique des mesures. Il convient donc de l’éliminer de manière à garder présente l’admissibilité physique des images polarimétriques afin de tirer pleinement profit de leur richesse informationnelles. Une méthode de filtrage paramétrique est proposée dans ce papier. Cette méthode repose sur une combinaison de deux techniques fréquemment utilisées en filtrage d’images multispectrales ; le Scatter plot est le masquage des données, sur lesquelles une vectorisation fractale est appliquée. La méthode proposée présente un compromis entre le filtrage des images polarimétriques et la conservation de leur contenu physique. Les performances statistiques de la méthode sont testées sur des images de Stokes simulées et réelles par l’algorithme du ré-echantillonage Bootstrap

Deep learning architectures analysis for age-related macular degeneration segmentation on optical coherence tomography scans

International audienc

Salient objects detection in dynamic scenes using color and texture features

International audienceVisual saliency is an important research topic in the field of computer vision due to its numerous possible applications. It helps to focus on regions of interest instead of processing the whole image or video data. Detecting visual saliency in still images has been widely addressed in literature with several formulations. However , visual saliency detection in videos has attracted little attention, and is a more challenging task due to additional temporal information. A common approach for obtaining a spatio-temporal saliency map is to combine a static saliency map and a dynamic saliency map. In our work, we model the dynamic textures in a dynamic scene with local binary patterns to compute the dynamic saliency map, and we use color features to compute the static saliency map. Both saliency maps are computed using a bio-inspired mechanism of human visual system with a discriminant formulation known as center surround saliency, and are fused in a proper way. The proposed model has been extensively evaluated with diverse publicly available datasets which contain several videos of dynamic scenes, and comparison with state-of-the art methods shows that it achieves competitive results

classification with SVM, Boosting and Hyperrectangle based method

Real-time flaw detection on complex object: comparison of results usin

Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models.

The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). With the use of structural magnetic resonance (MR) imaging, we can investigate the effect of AD on the morphology of the hippocampus. The hippocampal shape variations among a population can be usually described using statistical shape models (SSMs). Conventional SSMs model the modes of variations among the population via principal component analysis (PCA). Although these modes are representative of variations within the training data, they are not necessarily discriminative on labeled data or relevant to the differences between the subpopulations. We use the shape descriptors from SSM as features to classify AD from normal control (NC) cases. In this study, a Hotelling's T2 test is performed to select a subset of landmarks which are used in PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination ability of these predictors is evaluated in terms of their classification performances with bagged support vector machines (SVMs). Restricting the model to landmarks with better separation between AD and NC increases the discrimination power of SSM. The predictors extracted on the subregions also showed stronger correlation with the memory-related measurements such as Logical Memory, Auditory Verbal Learning Test (AVLT) and the memory subscores of Alzheimer Disease Assessment Scale (ADAS)