53 research outputs found
Classification de mines sous-marines à partir de l'image sonar brute : caractérisation du contour de l'ombre portée par algorithme génétique
National audienceDans le domaine de la chasse aux mines sous-marines, l'objet dĂ©tectĂ© peut ĂȘtre caractĂ©risĂ© par son ombre portĂ©e sur le fond. L'approche classique est sĂ©quentielle : l'image sonar est tout d'abord segmentĂ©e afin d'obtenir une image binaire partageant les pixels entre la zone d'ombre et la zone de rĂ©verbĂ©ration de fond, puis des attributs caractĂ©ristiques sont extraits de la silhouette 2-D correspondant Ă l'ombre segmentĂ©e lesquels servent Ă classifier l'objet en fin de traitement. A chacune des Ă©tapes sont gĂ©nĂ©ralement associĂ©s des prĂ©- et/ou post-traitements visant Ă Ă©viter qu'une erreur intervenant Ă un instant donnĂ© de la chaĂźne de traitement se rĂ©percute jusqu'au rĂ©sultat final. Afin d'optimiser la procĂ©dure de classification en se concentrant sur l'objectif ultime de la chaĂźne de traitement, nous avons mis en Ćuvre un processus dynamique pour caractĂ©riser le contour de l'ombre Ă partir de l'image sonar brute en offrant en outre la possibilitĂ© de classifier l'objet dĂ©tectĂ©. Cette approche innovante fait appel aux notions de modĂšles dĂ©formables, modĂšles statistiques et algorithmes gĂ©nĂ©tiques
Mine Classification based on raw sonar data: an approach combining Fourier Descriptors, Statistical Models and Genetic Algorithms
International audienceIn the context of mine warfare, detected mines can be classified from their cast shadow. A standard solution is to perform image segmentation first (we obtain binary from graylevel image giving the label zero for pixels belonging to the shadow and the label one elsewhere), and then to perform a classification based on features extracted from the 2D-shape of the segmented shadow. Consequently, if a mistake happens during the process, it will be propagated through the following steps. In this paper, to avoid such drawbacks, we propose a novel approach where a dynamic segmentation scheme is fully classification-oriented. Actually, classification is performed directly from the raw image data. The approach is based on the combination of deformable models, genetic algorithms, and statistical image models
A 2-D Filter Specification for Sonar Image Thresholding
International audienceWe propose a new image sonar segmentation by combining two complementary competencies. On the one hand, following an image processing approach, we aim at partitioning raw image data to provide a binary image. On the other hand, we take advantage of technological knowledge such as the principle of sonar image formation. For sonar images, grey-level histogram generally presents a single mode which entails a poor separation of two theoretical modes related to reverberation and shadow subpopulations of the image. The separation of these two modes is of critical interest in a further description of objects from their cast shadows. In this paper, an optimal filter is specified by a criterion which aims at changing the statistical properties of each area while making threshold value selection from the histogram easier. While minimizing the output pixels variance, pixel values in each region concentrate around the respective average value while, simultaneously, two distinct modes appear on the histogram of the filtered image. The minimum value found between the two modes in the smoothed histogram leads to the searched threshold. In addition, we show how filter aspect depends as well on the image sonar resolution as on sonar parameters
Mine Classification based on a Fuzzy Characterisation
International audienceHigh resolution sonars provide high-quality acoustic images, allowing the classification of objects from their cast shadow. For a given ground mine except mine with radial symmetry, shadow appearance generally depends on the point of view. After a segmentation step performed on images acquired along a part of a circular trajectory of the sonar around the object, we can match and superimpose binary data. The resulting image displays a fuzzy shadow region whose pixels grey-levels depend on their successive localisation in the images of the sequence, i.e. if they belong or not to the shadow region. As an extension of feature extraction in the binary case, fuzzy geometry is a practical tool to describe fuzzy regions characterised by the degree of membership of each pixel to them. After a Principal Component Analysis applied to a set of fuzzy features, encouraging results have been achieved on simulated sonar images covering both classical and stealthy mines
Change Detection Using Synthetic Aperture Sonar: Preliminary Results from the Larvik Trial
International audienceIn April of 2011, FFI led a sea trial near Larvik, Norway on FFIs research vessel the H.U. Sverdrup II with participation by representatives from Canada, United States, and France. One objective of the sea trial was to acquire a data set suitable for examining incoherent and coherent change detection and automated target recognition (ATR) algorithms applied to Synthetic Aperture Sonar (SAS) imagery. The end goal is to produce an automated tool for detecting recently placed objects on the seafloor. To test these algorithms two areas were chosen, one with a comparatively benign seafloor and one with a boulder strewn complex seafloor. Each area was surveyed before and after deployment of objects. The survey time intervals varied from two days to eight days. In this paper we present the trial and show examples of SAS images and change detection of the images
Automatic Underwater Image Denoising
A novel pre-processing filter is proposed for underwater image restoration. Because of specific transmission properties
of light in the water, underwater image suffers from limited range,non uniform lighting, low contrast, color diminished,
important blur⊠Today pre-processing methods typically only concentrates on non uniform lighting or color correction
and often require additional knowledge of the environment. The algorithm proposed in this paper is an automatic
algorithm to pre-process underwater images. It reduces underwater perturbations, and improves image quality. It is
composed of several successive independent processing steps which correct non uniform illumination, suppress noise,
enhance contrast and adjust colors. Performances of filtering will be assessed using an edge detection robustness
criterion.L'obstacle majeur dans le traitement des images sous-marines résulte des phénomÚnes d'absorption
et de diffusion dus aux propriétés optiques particuliÚres de la lumiÚre dans l'eau. Ces deux phénomÚnes
auxquels s'ajoute le problÚme de turbidité, impose de travailler sur des images trÚs bruitées, avec souvent,
une illumination non uniforme, des contrastes faibles, des couleurs atténuées⊠Cet article présente
une nouvelle méthode automatique de pré-traitement des images sous marines. L'algorithme proposé
qui ne nécessite ni paramétrage manuel ni information a priori, permet d'atténuer les défauts précédemment
cités et d'améliorer de façon significative la qualité des images. L'éclairage, le bruit, les contrastes
puis les couleurs sont corrigés séquentiellement
Fusion multi-attributs d'images sonar
L'utilisation de sonar haute rĂ©solution pour la guerre des mines permet d'envisager la classification d'un objet immergĂ© Ă partir de l'ombre acoustique portĂ©e sur le fond. Le levĂ© des ambiguĂŻtĂ©s inhĂ©rentes Ă la classification mono vue peut ĂȘtre rĂ©alisĂ© en utilisant plusieurs images prĂ©sentant les ombres acquises selon des points de vue distincts. Nous proposons ainsi d'effectuer une classification supervisĂ©e multivues par fusion des attributs caractĂ©ristiques extraits d'images sonar constituant trois points de vue imprĂ©cis (angles de vue inconnus) d'une mĂȘme cible. La mĂ©thode repose sur l'analyse de la rĂ©partition d'attributs caractĂ©ristiques extraits d'images sonar haute rĂ©solution dans le cadre de la thĂ©orie des ensembles flous et la dĂ©finition de modĂšles de fusion adaptĂ©s. L'Ă©valuation de ces modĂšles a Ă©tĂ© rĂ©alisĂ©e sur une base d'images simulĂ©es avec cinq types de mines
Perception de lâenvironnement marin et de la menace Ă partir dâimages sonar haute frĂ©quence : du fond marin Ă la surface de mer
Similarly to marine mammals, mankind has developed sonar systems which are able to perceive underwater environments on a range of distances and with more or less detail, depending on its needs. The need addressed in this manuscript is automatic underwater target (ATR) recognition, whether the target is laying on the seafloor or floating in the water column. The chronology of the work which is presented shows how sonar systems and underlying algorithms have progressively been improved in order to adapt to new threats, from the stealthy mines of the 90s to the improvised explosive devices of today. To counter these threats using side-scan or synthetic aperture sonar (SAS) data, two topics in particular are discussed: first, a new mine-hunting ATR approach which takes into consideration the environment in the vicinity of the target; second, the analysis and use of repeat-pass SAS data acquired in a monitoring context in order to perform change detection. Apart from the detection of targets lying on the seabed, obstacle detection issues have more recently been tackled in order to allow underwater drones to operate in complete safety whatever the depth. For each type of mission âseabed tracking, obstacle detection and tracking in the water column, and vehicle surfacingâ, the combination of configuration and mode of the obstacle-avoidance sonar systems, and dedicated algorithms are described.The body of work presented is based on high-frequency sonar imaging, ranging from side-scan sonars to forward-looking sonars, and most of it is the result of various collaborations. Initially intended for operators, the use of sonar images needs to be adapted to an increasing quantity of data to be processed, and to the increasing automation of tasks aboard underwater drones. The stealth, flexibility and safety of these small-size vehicles open up new opportunities both of missions and of choice of relevant imaging and navigation sensors, not only in the military but also in the civilian domain. More generally, this adaptation process to underwater mobile robotics will require the analysis of perception-action mechanisms in an uncertain and unstructured environment, with limited communication.A lâinstar des mammifĂšres marins, lâhomme a dĂ©veloppĂ© des systĂšmes sonar capables de percevoir lâenvironnement marin sur des distances plus ou moins longues et avec plus ou moins de dĂ©tails en fonction de ses besoins. Le principal besoin traitĂ© dans ce travail prĂ©parĂ© pour lâhabilitation Ă diriger les recherches est la reconnaissance automatique de cibles sous-marines (plus connue sous lâacronyme ATR pour Automatic Target Recognition), quâelles soient posĂ©es sur le fond ou flottantes entre deux eaux.La chronologie de la sĂ©lection de travaux prĂ©sentĂ©s suit le perfectionnement au fil des annĂ©es des systĂšmes dâimagerie et des algorithmes de traitement associĂ©s pour rĂ©pondre Ă lâĂ©volution de la menace, depuis les mines furtives des annĂ©es 90 aux engins explosifs improvisĂ©s dâaujourdâhui. Pour contrer ces menaces Ă partir de donnĂ©es sonar latĂ©ral ou sonar Ă antenne synthĂ©tique (dites donnĂ©es SAS pour Synthetic Aperture Sonar), deux points sont en particulier exposĂ©s : une nouvelle approche de lâATR pour les missions de chasse aux mines qui passe par la qualification de lâenvironnement proche de la mine dâune part, la comprĂ©hension et lâexploitation de donnĂ©es SAS acquises lors de passes rĂ©pĂ©titives pour la mise en Ćuvre dâalgorithmes de dĂ©tection (incohĂ©rente puis cohĂ©rente) de changements dans un contexte de surveillance dâautre part. Outre la reconnaissance de cibles sur le fond marin, la problĂ©matique de la dĂ©tection dâobstacles sâest imposĂ©e plus rĂ©cemment afin de permettre Ă des drones sous-marins dâautonomie croissante de mener leur mission en toute sĂ©curitĂ© et ce, quelle que soit leur immersion. Dans ce manuscrit, la configuration et le mode des systĂšmes sonar dâĂ©vitement, de mĂȘme que les algorithmes dĂ©diĂ©s Ă chaque mission - suivi de fond, pistage d'obstacles dans la colonne dâeau, et enfin reprise de vue-, sont discutĂ©s.La synthĂšse des travaux effectuĂ©e pour cette habilitation Ă diriger les recherches repose ainsi sur lâexploitation des systĂšmes dâimagerie sonar haute frĂ©quence (HF), des sonars latĂ©raux aux sonars frontaux, et est le plus souvent le fruit de diverses collaborations. Initialement destinĂ© aux opĂ©rateurs, lâusage des images sonar HF est aujourdâhui Ă adapter face au flot croissant de donnĂ©es Ă traiter et Ă lâautomatisation progressive des tĂąches Ă bord de drones sous-marins. La discrĂ©tion, la souplesse et la sĂ©curitĂ© de ces vĂ©hicules ouvrent de nombreuses possibilitĂ©s en termes de missions et dâutilisation de capteurs ad hoc dâimagerie et de navigation, y compris pour le domaine civil. Plus globalement, cette adaptation Ă la robotique mobile sous-marine exigera lâĂ©tude des mĂ©canismes de perception-action dans un environnement incertain et peu structurĂ© avec une communication limitĂ©e
Incoherent change detection using amplitude sidescan sonar images
International audienceThe automatic detection of temporal changes in sonar images is of high interest for monitoring critical areas as ports or channels used by submarines for instance. This problem is addressed here as incoherent change detection between two sonar passes. To be able to compare two surveys, sonar images have first to be registered to each other. A mean shift between the master image and the slave image has been estimated here at a coarse-to-fine image resolution by minimizing the Kullback-Leibler divergence between the first and the second sets of amplitude distributions related to homogeneous areas of the master image. Then two different methods have been proposed to detect changes in terms of contacts in the common area of the registered images. The first method is one of the well-known techniques used for synthetic aperture radar imagery: the log ratio of images. The second one is made of two stages: firstly a goodness-of-fit test is applied to every image divided into small overlapped snippets in order to detect statistical deviations due to possible contacts; secondly a pixelwise difference is simply performed between these two images of detected snippets put in a non-zero pixel value. For both methods morphological operations and local correlations have been performed in order to discard false alarms. Advantages and drawbacks of the two proposed methods are discussed with results on a set of data collected by DRDC-Atlantic with a high-frequency sidescan sonar a month apart in the winter of 2008 in a port area
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