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

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

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

Automated fish age estimation from otolith images using statistical learning

International audienceThe acquisition of age and growth data is of key importance for fisheries research (assessment, marine ecology issues, etc.). Consequently, automating this task is of great interest. In this paper, we investigate the use of statistical learning techniques for fish age estimation. The core of this study lies in the definition of relevant image-related features. We rely on the computation of a 1D representation summing up the content of otolith images within a predefined area of interest. Features are then extracted from this non-stationary representation depicting the alternation of seasonal growth rings. Thus, fish age estimation can be viewed as a multi-class classification issue using statistical learning strategies. In particular, a procedure based on demodulation and remodulation of fish growth patterns is used to improve the generalization properties of the trained classifiers. The experimental evaluation is carried out over a dataset of 320 plaice otolith images from age groups 1–6. We analyze both, the performances of several statistical classifiers, namely SVMs (support vector machines) and neural networks, and the relevance of the proposed image-based feature sets. In addition, the combination of additional biological and shape features to the image-related ones is considered. We reach a rate of correct age estimation of 88% w.r.t. the expert ground truth. This demonstrates the relevance of the proposed approach for the automation of routine aging and for computer-assisted aging

Unsupervised calibrated sonar imaging for seabed observation using hidden Markov random fields

International audienceThis paper deals with seabed imaging issued from sonar systems. Such imaging systems produce images of backscattering (BS) strength relative to physical seabed characteristics. However, these Bs measurements are not only seabed-related but also dependent on the incident angle. Therefore, to enhance the quality of such seabed imaging systems, we develop an unsupervised approach to compensate for these seabed-related angular dependencies. Our approach combines robust estimation and hidden Markov random fields. Results on real data demonstrate the relevance of our approach to improve seabed observation

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

Interpolation de données manquantes dans des séquences multi-modales d'images géophysiques satellitaires

Session "Articles"National audienceCet article étudie l'estimation conjointe de données manquantes et de champs de déplacements dans des séquences multimodales d'observations satellitaires géophysiques. La complexité de la tâche est liée au taux élevé de données manquantes (entre 20% et 90%) pour des observations journalières de haute résolution et la reconstruction de structures fines en accord avec la dynamique sous jacente. Nous avons développé une méthode basée sur l'assimilation variationnelle de données pour des séries multimodales et multi-résolutions. A l'aide de données synthétiques et de données réelles de la surface océanique, une évaluation numérique et qualitative démontre l'apport de deux composantes clés du modèle proposé: la fusion d'informations multimodales à partir d'une contrainte géométrique basée sur les structures frontales, et la méthode d'assimilation variationnelle utilisant comme à priori dynamique un modèle d'advection-diffusion. Les expérimentations conduites montrent que de bonnes performances de reconstruction sont obtenues pour les observations hautes résolutions en dépit du pourcentage élevé de données manquante