Semi-local extraction of ring structures in images of biological hard tissues: application to the Bayesian interpretation of fish otoliths for age and growth estimation

International audienceThis paper deals with the analysis of images of biological tissue that involves ring structures, such as tree trunks, bivalve seashells or fish otoliths, with a view to automating the acquisition of age and growth data. A bottom-up template-based scheme extracts meaningfulridge and valley curve data using growth-adapted time-frequency filtering. Age and growth estimation is then stated as the Bayesian selection of a subset of ring curves, combining ameasure of curve significativity and ana prioristatistical growth model. Experiments on realsamples demonstrate the efficiency of the proposed extraction stage. Our Bayesian frameworkis shown to significantly outperform previous methods for the interpretation of a dataset of200 plaice otoliths and compares favorably to inter-expert agreement rates (88% of agreement to expert interpretations)

Extraction and interpretation of ring structures in images of biological hard tissues: application to fish age and growth estimation.

International audienceThis paper presents a general framework for the automated estimation of age and growth from images of biological materials depicting concentric ring-like structures such as tree trunks, corals, bivalve seashells, fish scales or otoliths. This interpretation task can be seen as a ring segmentation issue, where growth rings are associated to image ridge and valley structures. This is stated as the Bayesian selection of a subset of partial ring curves extracted using a semi-local template-based growth-adapted scheme. The application to fish otolith interpretation provides a consistent and convincing validation of the proposed framework

Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data

Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal convolutional models and investigate different dictionary-based decompositions, namely based on principal component analysis (PCA), sparse priors and non-negativity constraints. We consider an application to the reconstruction of sea surface height (SSH) fields from two information sources, along-track altimeter data and sea surface temperature (SST) data. The reported experiments demonstrate the relevance of the proposed model, especially locally-adapted parametrizations with non-negativity constraints, to outperform optimally-interpolated reconstructions.Comment: 4 pages, 3 figure

The analog data assimilation

In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA). The proposed framework produces a reconstruction of the system dynamics in a fully data-driven manner where no explicit knowledge of the dynamical model is required. Instead, a representative catalog of trajectories of the system is assumed to be available. Based on this catalog, the analog data assimilation combines the nonparametric sampling of the dynamics using analog forecasting methods with ensemble-based assimilation techniques. This study explores different analog forecasting strategies and derives both ensemble Kalman and particle filtering versions of the proposed analog data assimilation approach. Numerical experiments are examined for two chaotic dynamical systems: the Lorenz-63 and Lorenz-96 systems. The performance of the analog data assimilation is discussed with respect to classical model-driven assimilation. A Matlab toolbox and Python library of the AnDA are provided to help further research building upon the present findings.Fil: Lguensat, Redouane. Université Bretagne Loire; FranciaFil: Tandeo, Pierre. Université Bretagne Loire; FranciaFil: Ailliot, Pierre. University of Western Brittany. Laboratoire de Mathématiques de Bretagne Atlantique; FranciaFil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; ArgentinaFil: Fablet, Ronan. Université Bretagne Loire; Franci

Joint interpolation of multi-sensor sea surface geophysical fields using non-local and statistical priors

This work addresses the joint analysis of multi-source and multi-resolution remote sensing data for the interpolation of high-resolution geophysical fields. As case-study application, we consider the interpolation of sea surface temperature fields. We propose a novel statistical model, which combines two key features: an exemplar-based prior and second-order statistical priors. The exemplar-based prior, referred to as a non-local prior, exploits similarities between local patches (small field regions) to interpolate missing data areas from previously observed exemplars. This non-local prior also sets an explicit conditioning between the multi-sensor data. Two complementary statistical priors, namely a prior on the spatial covariance and a prior on the marginal distribution of the high-resolution details, are considered as sea surface geophysical fields are expected to depict specific spectral and marginal features in relation to the underlying turbulent ocean dynamics. We report experiments on both synthetic data and real SST data. These experiments demonstrate the contributions of the proposed combination of non-local and statistical priors to interpolate visually-consistent and geophysically-sound SST fields from multi-source satellite data. We further discuss the key features and parameterizations of this model as well as its relevance with respect to classical interpolation techniques

Renouveler les Pratiques de Gestion des Experts : Une Approche par le Rayonnement

Scientific experts are in small number in organizations but they can make the difference in highly competitive contexts. These employees have developed through study and experience a high level of knowledge in a specific scientific field. Their skills are scarce and strategic for the company. Formerly dominated by the system of dual ladder, expertise management needs nowadays to be renewed. As compared to the careers of managers, the careers of experts suffer from a lack of recognition limiting the attractiveness of scientific careers in the private sector. Faced with the challenges and limitations of current management of expertise, the aim of the article is to demonstrate that the glance of experts can emerge as an alternative concept. The concept of glance is based on the fact that the confrontation with peers builds expertise. This implies that experts should be offered the possibility to participate to scientific activities outside the company even though they work for ultra-secure R&D departments. The article brings answers to issues such as the interests and the risks for the company in the emergence of this interface and approaches to safely manage glance of experts

Motion characterization from temporal cooccurrences of local motion-based measures for video indexing

This paper describes an original approach for motion interpretation with a view to content-based video indexing. We exploit a statistical analysis of the temporal distribution of appropriate local motion-based measures to perform a global motion characterization. We consider motion features extracted from temporal cooccurrence matrices, and related to properties of homogeneity, acceleration or complexity. Results on various real video sequences are reported and provide a first validation of the approach. 1

La Relation entre Mobilisation Collective, Engagement Multiple et Intention de Quitter des Consultants. Le cas d’une SSII

Major companies in the IT sector are facing a big problem and the question is to know how to retain professional consultants operate while ensuring that they are mobilized for collective action ? The objective of this study is based on the relationship bet ween multiple commitment, collective mobilization and intention to leave