Thèses sur le sujet « Détection de points de repère »
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Irrera, Paolo. « Traitement d'images de radiographie à faible dose : Débruitage et rehaussement de contraste conjoints et détection automatique de points de repère anatomiques pour l'estimation de la qualité des images ». Thesis, Paris, ENST, 2015. http://www.theses.fr/2015ENST0031/document.
Texte intégralWe aim at reducing the ALARA (As Low As Reasonably Achievable) dose limits for images acquired with EOS full-body system by means of image processing techniques. Two complementary approaches are studied. First, we define a post-processing method that optimizes the trade-off between acquired image quality and X-ray dose. The Non-Local means filter is extended to restore EOS images. We then study how to combine it with a multi-scale contrast enhancement technique. The image quality for the diagnosis is optimized by defining non-parametric noise containment maps that limit the increase of noise depending on the amount of local redundant information captured by the filter. Secondly, we estimate exposure index (EI) values on EOS images which give an immediate feedback on image quality to help radiographers to verify the correct exposure level of the X-ray examination. We propose a landmark detection based approach that is more robust to potential outliers than existing methods as it exploits the redundancy of local estimates. Finally, the proposed joint denoising and contrast enhancement technique significantly increases the image quality with respect to an algorithm used in clinical routine. Robust image quality indicators can be automatically associated with clinical EOS images. Given the consistency of the measures assessed on preview images, these indices could be used to drive an exposure management system in charge of defining the optimal radiation exposure
Irrera, Paolo. « Traitement d'images de radiographie à faible dose : Débruitage et rehaussement de contraste conjoints et détection automatique de points de repère anatomiques pour l'estimation de la qualité des images ». Electronic Thesis or Diss., Paris, ENST, 2015. http://www.theses.fr/2015ENST0031.
Texte intégralWe aim at reducing the ALARA (As Low As Reasonably Achievable) dose limits for images acquired with EOS full-body system by means of image processing techniques. Two complementary approaches are studied. First, we define a post-processing method that optimizes the trade-off between acquired image quality and X-ray dose. The Non-Local means filter is extended to restore EOS images. We then study how to combine it with a multi-scale contrast enhancement technique. The image quality for the diagnosis is optimized by defining non-parametric noise containment maps that limit the increase of noise depending on the amount of local redundant information captured by the filter. Secondly, we estimate exposure index (EI) values on EOS images which give an immediate feedback on image quality to help radiographers to verify the correct exposure level of the X-ray examination. We propose a landmark detection based approach that is more robust to potential outliers than existing methods as it exploits the redundancy of local estimates. Finally, the proposed joint denoising and contrast enhancement technique significantly increases the image quality with respect to an algorithm used in clinical routine. Robust image quality indicators can be automatically associated with clinical EOS images. Given the consistency of the measures assessed on preview images, these indices could be used to drive an exposure management system in charge of defining the optimal radiation exposure
Jacinto, Hector. « Positionnement automatique de points de repère anatomiques pour la plannification chirurgicale ». Thesis, Lyon, INSA, 2015. http://www.theses.fr/2015ISAL0077/document.
Texte intégralNowadays, orthopedic surgeons utilize patient-specific systems based on custom cutting guides for total knee arthroplasty. Particularly, the chirurgical operation is prepared by using planning tools where the surgeon can manipulate virtual images of the patient, built from pre-surgical medical images. Identified anatomical landmarks provide various measurements on the virtual lower limb allowing to control the positioning of the cutting guides relative to the 3-D models of the knee bones. We propose a multi-atlas method for the automatic positioning of the pre-defined landmarks on the surface of the models of the femur and the tibia of the patient. We exploit a group of atlases (expert examples) consisting of multiple triangular meshes for which the defined landmarks have been placed by experts. We transfer identified landmarks from an expert example to the patient mesh by computing an initial coarse global registration with an Iterative Closest Point (ICP) algorithm where a curvature constraint serves as a supplementary dimension in order to improve robustness. Adaptive local rigid registrations refine the fit for the projection of reference landmarks onto the surface of the patient mesh. After the patient mesh has been processed with the group of atlases, we compute a definite position for each landmark using an automatic selection of a set of best transferred landmarks. We developed a Web framework for the construction of the 3-D models of the bones of the patient. Our positioning method is implemented in this system. We compare our positioning method against the manual positioning of trained operators and against the results of published scientific works on the domain
Loiseau-Witon, Nicolas. « Détection et description de points clés par apprentissage ». Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0101.
Texte intégralHospitals are increasingly generating 3D medical images that require automatic registration for systematic and large-scale analysis. Key points are used to reduce the time and memory required for this registration, and can be detected and described using various classical methods, as well as neural networks, as demonstrated numerous times in 2D. This thesis presents results and discussions on methods for detecting and describing key points using 3D neural networks. Two types of networks were studied to detect and/or describe characteristic points in 3D medical images. The first networks studied describe the areas directly surrounding key points, while the second type performs both detection and description of key points in a single step
Mérigot, Quentin. « Détection de structure géométrique dans les nuages de points ». Phd thesis, Université de Nice Sophia-Antipolis, 2009. http://tel.archives-ouvertes.fr/tel-00443038.
Texte intégralFraisier-Vannier, Ophélie. « Détection de points de vue sur les médias sociaux numériques ». Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30200.
Texte intégralNumerous domains have interests in studying the viewpoints expressed online, be it for marketing, cybersecurity, or research purposes with the rise of computational social sciences. We propose in this manuscript two contributions to the field of stance detection, focused around the difficulty of obtaining annotated data of quality on social medias. Our first contribution is a large and complex dataset of 22853 Twitter profiles active during the French presidential campaign of 2017. This is one of the rare datasets that considers a non-binary stance classification and, to our knowledge, the first one with a large number of profiles, and the first one proposing overlapping political communities. This dataset can be used as-is to study the campaign mechanisms on Twitter, or used to test stance detection models or network analysis tools. We then propose two semi-supervised generic stance detection models using a handful of seed profiles for which we know the stance to classify the rest of the profiles by exploiting various proximities. Indeed, current stance detection models are usually grounded on the specificities of some social platforms, which is unfortunate since it does not allow the integration of the multitude of available signals. By infering proximities from differents types of elements available on social medias, we can detect profiles close enough to assume they share a similar stance on a given subject. Our first model is a sequential ensemble algorithm which propagates stances thanks to a multi-layer graph representing proximities between profiles. Using datasets from two platforms, we show that, by combining several types of proximities, we can achieve excellent results. Our second model allows us to observe the evolution of profiles' stances during an event with as little as one seed profile by stance. This model confirms that a large majority of profiles do not change their stance on social medias, or do not express their change of heart
Mille, Frédéric. « Systèmes de détection des interactions médicamenteuses : points faibles & ; propositions d'améliorations ». Phd thesis, Université Pierre et Marie Curie - Paris VI, 2008. http://tel.archives-ouvertes.fr/tel-00354268.
Texte intégralEn effet, il apparaît que leur implantation impacte l'organisation du travail médical et que les recommandations réalisées par les systèmes d'aide à la décision-Clinical Decision Support Systems (CDSS) ne sont pas toujours cliniquement pertinentes. Les conséquences de ces modifications/anomalies vont du rejet du système par les utilisateurs à une diminution de la sécurité du patient. Les systèmes de détection des interactions font partie des premiers CDSS ayant été mis au point. Ils sont également le sujet de critiques relatives à la non-pertinence de leurs interventions.
Dans ce contexte, nous avons essayé de proposer des modifications algorithmiques pour ces systèmes ainsi que les connaissances nécessaires à leur fonctionnement, dans le but d'améliorer la pertinence clinique de leurs recommandations. L'objectif de ce travail est de proposer un algorithme et la base de connaissances nécessaire à son fonctionnement, pour améliorer la spécificité des systèmes de détection des IAM. Ce travail couvre aussi bien la construction de l'algorithme que la modélisation et l'acquisition des connaissances nécessaire à son fonctionnement.
La méthodologie que nous avons suivie, est triple. Dans un premier temps, nous avons effectué une analyse des alertes signalées par un système de détection des IAM, utilisé dans un hôpital parisien. L'objectif de cette étude était de mettre en évidence les raisons motivant les utilisateurs pour passer outre les alertes produites par le système. A partir de cette étude, nous avons pu proposer un ensemble de spécifications et un algorithme répondant à ces spécifications.
Dans un deuxième temps, nous avons procédé à la modélisation et à l'acquisition des connaissances relatives aux IAM. Cette deuxième étape fut réalisée en utilisant les techniques de l'ingénierie des connaissances et plus particulièrement les techniques de l'ingénierie documentaire. Le résultat pratique de cette deuxième étape est la base de connaissances sur les IAM et l'éditeur de fichiers XML ayant servi à l'encoder. Cet éditeur utilise le modèle que nous avons développé grâce à une application des techniques d'extraction des connaissances à partir des textes.
En dernier lieu, nous avons recherché, au travers d'une étude descriptive, s'il existe un lien entre les alertes (association médicamenteuse), le service où l'alerte est signalée et la réponse (acceptation/rejet) de l'utilisateur. Il apparaît que l'existence de ce lien est probable, ce qui ouvre la voie à l'adaptation des alertes (intrusives/non intrusives) en fonction de l'utilisateur.
Les résultats théoriques que nous avons obtenus, nous encouragent à poursuivre nos recherches dans ce domaine, pour mettre en application l'algorithme et la base de connaissances que nous avons développés. Nous sommes également motivés pour étendre notre champ de recherche dans le domaine du data-mining, afin de permettre aux systèmes informatisés de connaître « les méthodes de prescription des médecins ».
Rousseau, Sylvain. « Détection de points d'intérêt par acquisition compressée dans une image multispectrale ». Phd thesis, Université de Poitiers, 2013. http://tel.archives-ouvertes.fr/tel-00968176.
Texte intégralMille, Frédéric. « Systèmes de détection des interactions médicamenteuses : points faibles et propositions d'améliorations ». Paris 6, 2008. http://www.theses.fr/2008PA066634.
Texte intégralWalter, Nicolas. « Détection de primitives par une approche discrète et non linéaire : application à la détection et la caractérisation de points d'intérêt dans les maillages 3D ». Phd thesis, Université de Bourgogne, 2010. http://tel.archives-ouvertes.fr/tel-00808216.
Texte intégralHamdoun, Omar. « Détection et ré-identification de piétons par points d'intérêt entre caméras disjointes ». Phd thesis, École Nationale Supérieure des Mines de Paris, 2010. http://pastel.archives-ouvertes.fr/pastel-00566417.
Texte intégralXiao, Wen. « Détection de changements à partir de nuages de points de cartographie mobile ». Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1125/document.
Texte intégralMobile mapping systems are increasingly used for street environment mapping, especially mobile laser scanning technology enables precise street mapping, scene understanding, facade modelling, etc. In this research, the change detection from laser scanning point clouds is investigated. First of all, street environment change detection using RIEGL data is studied for the purpose of database updating and temporary object identification. An occupancy-based method is presented to overcome the challenges encountered by the conventional distance-based method, such as occlusion, anisotropic sampling. Occluded areas are identified by modelling the occupancy states within the laser scanning range. The gaps between points and scan lines are interpolated under the sensor reference framework, where the sampling density is isotropic. Even there are some conflicts on penetrable objects, e.g. trees, fences, the occupancy-based method is able to enhance the point-to-triangle distance-based method. The change detection method is also applied to data acquired by different laser scanners at different temporal-scales with the intention to have wider range of applications. The local sensor reference framework is adapted to Velodyne laser scanning geometry. The occupancy-based method is implemented to detection moving objects. Since the method detects the change of each point, moving objects are detect at point level. As the Velodyne scanner constantly scans the surroundings, the trajectories of moving objects can be detected. A simultaneous detection and tracking algorithm is proposed to recover the pedestrian trajectories in order to accurately estimate the traffic flow of pedestrian in public places. Changes can be detected not only at point level, but also at object level. The changes of cars parking on street sides at different times are detected to help regulate on-street car parking since the parking duration is limited. In this case, cars are detected in the first place, then they are compared with corresponding ones. Apart from car changes, parking positions and car types are also important information for parking management. All the processes are solved in a supervised learning framework. Furthermore, a model-based car reconstruction method is proposed to precisely locate cars. The model parameters are also treated as car features for better decision making. Moreover, the geometrically accurate models can be used for visualization purposes. Under the theme of change detection, related topics, e.g. tracking, classification, modelling, are also studied for the reason of practical applications. More importantly, the change detection methods are applied to different data acquisition geometries at multiple temporal-scales. Both bottom-up (point-based) and top-down (object-based) change detection strategies are investigated
Elmi, Mohamed Abdillahi. « Détection des changements de points multiples et inférence du modèle autorégressif à seuil ». Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD005/document.
Texte intégralThis thesis has two parts: the first part deals the change points problem and the second concerns the weak threshold autoregressive model (TAR); the errors are not correlated.In the first part, we treat the change point analysis. In the litterature, it exists two popular methods: The Penalized Least Square (PLS) and the Filtered Derivative introduced by Basseville end Nikirov.We give a new method of filtered derivative and false discovery rate (FDqV) on real data (the wind turbines and heartbeats series). Also, we studied an extension of FDqV method on weakly dependent random variables.In the second part, we spotlight the weak threshold autoregressive (TAR) model. The TAR model is studied by many authors such that Tong(1983), Petrucelli(1984, 1986). there exist many applications, for example in economics, biological and many others. The weak TAR model treated is the case where the innovations are not correlated
Moradkhan, Romel. « Détection des points critiques d'une forme : application à la reconnaissance de caractères manuscrits ». Paris 9, 1993. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=1993PA090012.
Texte intégralThe représentation of two-dimensional patterns by their contours is of great importance since many patterns, such as hand-written or printed characters, can be recognized by their contours. Because of its complexity the détection of dominant points of digitalized contours continues to be an important area of research. The first part of our work covers dominant point détection methods of digitalized curves (contours). After a survey of existing techniques we propose two new and efficient methods: the first is based on the notion of "co-angularity"; the second on the notion of "axis of symmetry". In the second part we focus on the problem of hand-written character récognition. We have proposed a hierarchical algorithm based on ctural matching which is both flexible and continuous
Rousseau, Sylvain. « Détection de points d'intérêts dans une image multi ou hyperspectral par acquisition compressée ». Thesis, Poitiers, 2013. http://www.theses.fr/2013POIT2269/document.
Texte intégralMulti- and hyper-spectral sensors generate a huge stream of data. A way around thisproblem is to use a compressive acquisition of the multi- and hyper-spectral object. Theobject is then reconstructed when needed. The next step is to avoid this reconstruction and towork directly with compressed data to achieve a conventional treatment on an object of thisnature. After introducing a first approach using Riemannian tools to perform edge detectionin multispectral image, we present the principles of the compressive sensing and algorithmsused to solve its problems. Then we devote an entire chapter to the detailed study of one ofthem, Bregman type algorithms which by their flexibility and efficiency will allow us to solvethe minimization encountered later. We then focuses on the detection of signatures in amultispectral image relying on an original algorithm of Guo and Osher based on minimizingL1. This algorithm is generalized in connection with the acquisition compressed. A secondgeneralization will help us to achieve the pattern detection in a multispectral image. Andfinally, we introduce new matrices of measures that greatly simplifies calculations whilemaintaining a good quality of measurements
Ma, Qixiang. « Deep learning based segmentation and detection of aorta structures in CT images involving fully and weakly supervised learning ». Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS029.
Texte intégralEndovascular aneurysm repair (EVAR) and transcatheter aortic valve implantation (TAVI) are endovascular interventions where preoperative CT image analysis is a prerequisite for planning and navigation guidance. In the case of EVAR procedures, the focus is specifically on the challenging issue of aortic segmentation in non-contrast-enhanced CT (NCCT) imaging, which remains unresolved. For TAVI procedures, attention is directed toward detecting anatomical landmarks to predict the risk of complications and select the bioprosthesis. To address these challenges, we propose automatic methods based on deep learning (DL). Firstly, a fully-supervised model based on 2D-3D features fusion is proposed for vascular segmentation in NCCTs. Subsequently, a weakly-supervised framework based on Gaussian pseudo labels is considered to reduce and facilitate manual annotation during the training phase. Finally, hybrid weakly- and fully-supervised methods are proposed to extend segmentation to more complex vascular structures beyond the abdominal aorta. When it comes to aortic valve in cardiac CT scans, a two-stage fully-supervised DL method is proposed for landmarks detection. The results contribute to enhancing preoperative imaging and the patient's digital model for computer-assisted endovascular interventions
Cayla, Denise. « Errance et points de repère chez Wim Wenders : analyse de trois films : Alice dans les villes, Au fil du temps, l'Etat des choses ». Montpellier 3, 1988. http://www.theses.fr/1988MON30038.
Texte intégralChapel, Marie-Neige. « Détection d’objets en mouvement à l’aide d’une caméra mobile ». Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1156/document.
Texte intégralMoving objects detection in video streams is a commonly used technique in many computer vision algorithms. The detection becomes more complex when the camera is moving. The environment observed by this type of camera appeared moving and it is more difficult to distinguish the objects which are in movement from the others that composed the static part of the scene. In this thesis we propose contributions for the detection of moving objects in the video stream of a moving camera. The main idea to differenciate between moving and static objects based on 3D distances. 3D positions of feature points extracted from images are estimated by triangulation and then their 3D motions are analyzed in order to provide a sparse static/moving labeling. To provide a more robust detection, the analysis of the 3D motions is compared to those of feature points previously estimated static. A confidance value updated over time is used to decide on labels to attribute to each point.We make experiments on virtual (from the Previz project 1) and real datasets (known by the community [Och+14]) and we compare the results with the state of the art. The results show that our 3D constraint coupled with a statistical and temporal analysis of motions allow to detect moving elements in the video stream of a moving camera even in complex cases where apparent motions of the scene are not similars
Henry, Caroline. « Détection de points brillants par corrélation complexe entre sous-vues d'une image RSO spatiale ». Toulouse, INPT, 2003. http://www.theses.fr/2003INPT050H.
Texte intégralGrunwald, Monique. « Détection des anomalies chromosomiques par cytométrie en flux et localisation des points de translocation ». Paris 6, 1986. http://www.theses.fr/1986PA066487.
Texte intégralLabeau, Olivier. « Détection et étude de nano-objets : nanocristaux de CdSe/ZnS et molécules uniques ». Bordeaux 1, 2005. http://www.theses.fr/2005BOR13004.
Texte intégralBigorgne, Erwan. « Détection et caractérisation de points singuliers pour l' appariement et l' indexation d' images couleurs ». Paris 6, 2005. http://www.theses.fr/2005PA066270.
Texte intégralRombourg, Romain. « Analyse, modélisation et détection de bruits pour scanners laser terrestres ». Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM064.
Texte intégralIn this thesis, we focused on several topics related to noise detection in point cloud generated by Terrestrial Laser Scanners (TLS). First, the projection methods to compute an image from a TLS scan. Second, the detection of sky noise, i.e. noise produced when a Amplitude Modulated Continuous Wave TLS measures range only from background radiation. And finally, the detection of mixed point noise, i.e. points acquired when the TLS was receiving return signals from several different surfaces. To tackle these challenges, we first analysed how the TLS samples space and deduced properties on how the local point cloud density evolves with respect to the elevation, this allowed us to show the limits of usual noise detection techniques and oriented our focus on 2D non density based detection techniques. We then defined a theoretical framework to analyse projection methods, unavoidable foundations for 2D detection methods. This framework allowed us to bring to light two fundamental properties that should be satisfied by a projection. Following these properties, we designed a projection algorithm that satisfied them as much as possible. We then defined a way to quantify projection quality and compared our proposed algorithm with the widely used classic algorithm and showed that the classic projection method is not adapted. Our proposed projection however showed very good results. Since the sky noise was never studied in previous works, we formally analysed it to build some theoretical foundations for sky detection. The analysis allowed us to show theoretically and experimentally that the range distribution of sky noise is independent of the underlying properties of the background radiation signal. From our projection and the discovered properties, we designed a sky detector and a mixed point detector. The detectors were tested via an extensive validation in controlled conditions. The results showed that our proposed detectors combined with the proposed projection are able to correctly detect almost all presented noise with few bad detection for the sky detectors and reasonable amount for the mixed point detector
Narongpunt, Veerasak. « Détection par thermographie infrarouge de la chaleur cutanée provoquée par la stimulation des points de méridien utilisés en acupuncture chinoise ». Paris 13, 2005. http://www.theses.fr/2005PA132028.
Texte intégralFu, Wenhao. « Visual servoing for mobile robots navigation with collision avoidance and field-of-view constraints ». Thesis, Evry-Val d'Essonne, 2014. http://www.theses.fr/2014EVRY0019/document.
Texte intégralThis thesis is concerned with the problem of vision-based navigation for mobile robots in indoor environments. Many works have been carried out to solve the navigation using a visual path, namely appearance-based navigation. However, using this scheme, the robot motion is limited to the trained visual path. The potential collision during the navigation process can make robot deviate from the current visual path, in which the visual landmarks can be lost in the current field of view. To the best of our knowledge, seldom works consider collision avoidance and landmark loss in the framework of appearance-based navigation. We outline a mobile robot navigation framework in order to enhance the capability of appearance-based method, especially in case of collision avoidance and field-of-view constraints. Our framework introduces several technical contributions. First of all, the motion constraints are considered into the visual landmark detection to improve the detection performance. Next then, we model the obstacle boundary using B-Spline. The B-Spline representation has no accidented regions and can generate a smooth motion for the collision avoidance task. Additionally, we propose a vision-based control strategy, which can deal with the complete target loss. Finally, we use spherical image to handle the case of ambiguity and infinity projections due to perspective projection. The real experiments demonstrate the feasability and the effectiveness of our framework and methods
Girardeau-Montaut, Daniel. « Détection de changement sur des données géométriques tridimensionnelles ». Phd thesis, Télécom ParisTech, 2006. http://pastel.archives-ouvertes.fr/pastel-00001745.
Texte intégralChapelle, Olivier. « Support Vector Machines : principes d'induction, Réglage automatique et connaissances à priori ». Paris 6, 2004. http://www.theses.fr/2004PA066524.
Texte intégralRichefeu, Julien. « Détection et analyse du mouvement sur système de vision à base de rétine numérique ». Phd thesis, Paris 6, 2006. http://pastel.archives-ouvertes.fr/pastel-00002557.
Texte intégralCalvet, Lilian. « Méthodes de reconstruction tridimensionnelle intégrant des points cycliques : application au suivi d’une caméra ». Phd thesis, Toulouse, INPT, 2014. http://oatao.univ-toulouse.fr/11901/1/Calvet.pdf.
Texte intégralFhima, Mehdi. « Détection de ruptures et mouvement Brownien multifractionnaire ». Thesis, Clermont-Ferrand 2, 2011. http://www.theses.fr/2011CLF22197.
Texte intégralThis Ph.D dissertation deals with "Off-line" detection of change points on parameters of time series of independent random variables, and in the Hurst parameter of multifrcational Brownian motion. It consists of three articles. In the first paper, published in Sequential Analysis, we set the cornerstones of the Filtered Derivative with p-Value method for the detection of change point on parameters of independent random variables. This method has linear time and memory complexities, with respect to the size of the series. It consists of two steps. The first step is based on Filtered Derivative method which detects the right change points as well as the false ones. We improve the Filtered Derivative method by adding a second step in which we compute the p-values associated to every single potential change point. Then we eliminate false alarms, i.e. the change points which have p-value smaller than a given critical level. We showed asymptotic properties needed for the calibration of the algorithm. The effectiveness of the method has been proved both on simulated data and on real data. Then we moved to the application of the method for the detection of change point on the Hurst parameter of multifractional Brownian motion. This was done in two phases. In the first phase, a paper is to be published in ESAIM P&S where we investigated the Central Limit Theorem of the Increment Ratio Statistic of a multifractional Brownian motion, leading to a CLT for the time varying Hurst index. The proofs are quite simple relying on Breuer-Major theorems and an original freezing of time strategy.The second phase relies on a new paper submitted for publication. We adapted the FDpV method to detect change points on the Hurst parameter of piecewise fractional Brownian motion. The underlying statistics of the FDpV technology is a new statistic estimator for Hurst index, so-called Increment Zero-Crossing Statistic (IZCS) which is a variation of IRS. Both FDpV and IZCS are methods with linear time and memory complexities, with respect to the size of the series
Pham, The Anh. « Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension ». Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4023/document.
Texte intégralLocal features are of central importance to deal with many different problems in image analysis and understanding including image registration, object detection and recognition, image retrieval, etc. Over the years, many local detectors have been presented to detect such features. Such a local detector usually works well for some particular applications but not all. Taking an application of image retrieval in large database as an example, an efficient method for detecting binary features should be preferred to other real-valued feature detection methods. The reason is easily seen: it is expected to have a reasonable precision of retrieval results but the time response must be as fast as possible. Generally, local features are used in combination with an indexing scheme. This is highly needed for the case where the dataset is composed of billions of data points, each of which is in a high-dimensional feature vector space
Lamarfa, Houda. « Développement d'une méthode multi-échelle de traitement des nuages de points LiDAR mobile pour la détection de convergence des tunnels miniers souterrains ». Master's thesis, Université Laval, 2019. http://hdl.handle.net/20.500.11794/34007.
Texte intégralSecurity in underground mines is one of the main concerns of mining companies. Monitoring any changes or distortions in mining tunnels and galleries, including tunnel’s convergence, is one of the top priorities for those companies. Commonly used convergence monitoring techniques in underground tunnels are based on limited measurements that are usually very time-consuming and costly in terms of process. Emerging mobile LiDAR technologies offer alternative solutions that are far more efficient and lower in risk. However, the accurate measurements of convergence in mining tunnels using mobile LiDAR data remains challenging because of the following issues: limited precision of LiDAR point clouds, the roughness and irregularity of the studied surfaces in the tunnels, the irregularity of the scan density, the presence of occlusions and the complexities of reliable registration of point clouds. This research work is defined as a part of the ‘MinEyes project’ in collaboration with the company Pecktech which main objective is to detect and measure the convergence of underground mining tunnels from mobile LiDAR data. To do so, we propose a multi-scale point cloud comparison method that considers the surface irregularities of the mining tunnels and allows a better detection of their changes and a more precise measurement of their convergence. The proposed method is based on a local model-to-model comparison method of underground mobile LiDAR point clouds. The results obtained are very promising compared to other LiDAR point cloud comparison methods (e.g. C2C, C2M methods). A statistical analysis of the results also has confirmed the efficiency of the proposed method, especially in the rough areas of the mining tunnels.
Ait, Fares Wassima. « Détection et suivi d'objets par vision fondés sur segmentation par contour actif basé région ». Phd thesis, Université Paul Sabatier - Toulouse III, 2013. http://tel.archives-ouvertes.fr/tel-00932263.
Texte intégralOesau, Sven. « Modélisation géométrique de scènes intérieures à partir de nuage de points ». Thesis, Nice, 2015. http://www.theses.fr/2015NICE4034/document.
Texte intégralGeometric modeling and semantization of indoor scenes from sampled point data is an emerging research topic. Recent advances in acquisition technologies provide highly accurate laser scanners and low-cost handheld RGB-D cameras for real-time acquisition. However, the processing of large data sets is hampered by high amounts of clutter and various defects such as missing data, outliers and anisotropic sampling. This thesis investigates three novel methods for efficient geometric modeling and semantization from unstructured point data: Shape detection, classification and geometric modeling. Chapter 2 introduces two methods for abstracting the input point data with primitive shapes. First, we propose a line extraction method to detect wall segments from a horizontal cross-section of the input point cloud. Second, we introduce a region growing method that progressively detects and reinforces regularities of planar shapes. This method utilizes regularities common to man-made architecture, i.e. coplanarity, parallelism and orthogonality, to reduce complexity and improve data fitting in defect-laden data. Chapter 3 introduces a method based on statistical analysis for separating clutter from structure. We also contribute a supervised machine learning method for object classification based on sets of planar shapes. Chapter 4 introduces a method for 3D geometric modeling of indoor scenes. We first partition the space using primitive shapes detected from permanent structures. An energy formulation is then used to solve an inside/outside labeling of a space partitioning, the latter providing robustness to missing data and outliers
Ait, Fares Wassima. « Détection et suivi d'objets par vision fondés sur segmentation par contour actif base région ». Toulouse 3, 2013. http://thesesups.ups-tlse.fr/2143/.
Texte intégralObject segmentation and tracking is a challenging area of ongoing research in computer vision. One important application lies in robotics where the ability to accurately segment an object of interest from its background is crucial and particularly on images acquired onboard during robot motion. Object segmentation technique consists in separating the object region from the image background according to a pre-defined criterion. Object tracking is a process of determining the positions of moving objects in image sequences. Several techniques can be applied to ensure these operations. In this thesis, we are interested to segment and track objects in video sequences using active contour method due to its robustness and efficiency to segment and track non-rigid objects. Active contour method consists in making a curve converge from an initial position around the object to be detected towards this object boundary according to a pre-defined criterion. We employ criteria which depend on the image regions what may impose certain constraints on the characteristics of these regions as a homogeneity assumption. This assumption may not always be verified due to the heterogeneity often present in images. In order to cope with the heterogeneity that may appear either in the object of interest or in the image background in noisy images using an inadequate active contour initialization, we propose a technique that combines local and global statistics in order to compute the segmentation criterion. By using a radius with a fixed size, a half-disk is superposed on each point of the active contour to define the local extraction regions. However, when the heterogeneity appears on both the object of interest and the image background, we develop a new technique based on a flexible radius that defines two half-disks with two different radius values to extract the local information. The choice of the value of these two radii is determined by taking into consideration the object size as well as the distance separating the object of interest from its neighbors. Finally, to track a mobile object within a video sequence using the active contour method, we develop a hybrid object tracking approach based on region characteristics and on motion vector of interest points extracted on the object region. Using our approach, the initial active contour for each image will be adequately adjusted in a way that it will be as close as possible to the actual boundary of the object of interest so that the evolution of active contour based on characteristics of the region will not be trapped by false contours. Simulation results on synthetic and real images validate the effectiveness of the proposed approaches
Almansa, Andrés. « Echantillonnage, interpolation et détection : applications en imagerie satellitaire ». Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2002. http://tel.archives-ouvertes.fr/tel-00665725.
Texte intégralLhéritier, Alix. « Méthodes non-paramétriques pour l'apprentissage et la détection de dissimilarité statistique multivariée ». Thesis, Nice, 2015. http://www.theses.fr/2015NICE4072/document.
Texte intégralIn this thesis, we study problems related to learning and detecting multivariate statistical dissimilarity, which are of paramount importance for many statistical learning methods nowadays used in an increasingly number of fields. This thesis makes three contributions related to these problems. The first contribution introduces a notion of multivariate nonparametric effect size shedding light on the nature of the dissimilarity detected between two datasets. Our two step method first decomposes a dissimilarity measure (Jensen-Shannon divergence) aiming at localizing the dissimilarity in the data embedding space, and then proceeds by aggregating points of high discrepancy and in spatial proximity into clusters. The second contribution presents the first sequential nonparametric two-sample test. That is, instead of being given two sets of observations of fixed size, observations can be treated one at a time and, when strongly enough evidence has been found, the test can be stopped, yielding a more flexible procedure while keeping guaranteed type I error control. Additionally, under certain conditions, when the number of observations tends to infinity, the test has a vanishing probability of type II error. The third contribution consists in a sequential change detection test based on two sliding windows on which a two-sample test is performed, with type I error guarantees. Our test has controlled memory footprint and, as opposed to state-of-the-art methods that also provide type I error control, has constant time complexity per observation, which makes our test suitable for streaming data
Gales, Guillaume. « Mise en correspondance de pixels pour la stéréovision binoculaire par propagation d'appariements de points d'intérêt et sondage de régions ». Phd thesis, Toulouse 3, 2011. http://tel.archives-ouvertes.fr/tel-00622859.
Texte intégralBenmansour, Fethallah. « Méthode des chemins minimaux appliquée à l'imagerie médicale : Segmentation de structures tubulaires et de surfaces par anisotropie multi-échelle et par détection récursive de points clés ». Paris 9, 2009. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=2009PA090034.
Texte intégralIn this thesis, we used and adapted the minimal path method to segment tubular structures in medical images, and to extract closed surfaces from biomedical images. The minimal path method has been introduced in order to minimize globally the geodesic active contour functional. In the first part of the manuscript, we recall the active contour models and their variants, and discuss their advantages and drawbacks. Then, we focus on the theoretical and numerical aspects of the minimal path method both in the classical isotropic case and the Riemannian anisotropic case. Also, we illustrate the importance of the metric and show how one can tune its parameters in order to overcome the shortcut issue. In the second part, we are interested in segmenting tubular structures. We propose a novel minimal path model that takes into account the vessel width and direction. We have chosen to exploit the tubular structure of the vessels one wants to extract to build an anisotropic metric giving higher speed on the center of the vessels and also when the minimal path tangent is coherent with the vessel’s direction. In the third part, the problem of surface extraction from 3D images is addressed. First, we introduce a front propagation approach to detect recursively keypoints on an object of interest. Then, we propose a method to extract a patch of surface from a single source point. In order to obtain a complete surface, this approach is iterated. Finally, we propose a global approach that takes benefit of the interfaces surrounding the object
Guislain, Maximilien. « Traitement joint de nuage de points et d'images pour l'analyse et la visualisation des formes 3D ». Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1219/document.
Texte intégralRecent years saw a rapid development of city digitization technologies. Acquisition campaigns covering entire cities are now performed using LiDAR (Light Detection And Ranging) scanners embedded aboard mobile vehicles. These acquisition campaigns yield point clouds, composed of millions of points, representing the buildings and the streets, and may also contain a set of images of the scene. The subject developed here is the improvement of the point cloud using the information contained in the camera images. This thesis introduces several contributions to this joint improvement. The position and orientation of acquired images are usually estimated using devices embedded with the LiDAR scanner, even if this information is inaccurate. To obtain the precise registration of an image on a point cloud, we propose a two-step algorithm which uses both Mutual Information and Histograms of Oriented Gradients. The proposed method yields an accurate camera pose, even when the initial estimations are far from the real position and orientation. Once the images have been correctly registered, it is possible to use them to color each point of the cloud while using the variability of the point of view. This is done by minimizing an energy considering the different colors associated with a point and the potential colors of its neighbors. Illumination changes can also change the color assigned to a point. Notably, this color can be affected by cast shadows. These cast shadows are changing with the sun position, it is therefore necessary to detect and correct them. We propose a new method that analyzes the joint variation of the reflectance value obtained by the LiDAR and the color of the points. By detecting enough interfaces between shadow and light, we can characterize the luminance of the scene and to remove the cast shadows. The last point developed in this thesis is the densification of a point cloud. Indeed, the local density of a point cloud varies and is sometimes insufficient in certain areas. We propose a directly applicable approach to increase the density of a point cloud using multiple images
El, Sayed Abdul Rahman. « Traitement des objets 3D et images par les méthodes numériques sur graphes ». Thesis, Normandie, 2018. http://www.theses.fr/2018NORMLH19/document.
Texte intégralSkin detection involves detecting pixels corresponding to human skin in a color image. The faces constitute a category of stimulus important by the wealth of information that they convey because before recognizing any person it is essential to locate and recognize his face. Most security and biometrics applications rely on the detection of skin regions such as face detection, 3D adult object filtering, and gesture recognition. In addition, saliency detection of 3D mesh is an important pretreatment phase for many computer vision applications. 3D segmentation based on salient regions has been widely used in many computer vision applications such as 3D shape matching, object alignments, 3D point-point smoothing, searching images on the web, image indexing by content, video segmentation and face detection and recognition. The detection of skin is a very difficult task for various reasons generally related to the variability of the shape and the color to be detected (different hues from one person to another, orientation and different sizes, lighting conditions) and especially for images from the web captured under different light conditions. There are several known approaches to skin detection: approaches based on geometry and feature extraction, motion-based approaches (background subtraction (SAP), difference between two consecutive images, optical flow calculation) and color-based approaches. In this thesis, we propose numerical optimization methods for the detection of skins color and salient regions on 3D meshes and 3D point clouds using a weighted graph. Based on these methods, we provide 3D face detection approaches using Linear Programming and Data Mining. In addition, we adapted our proposed methods to solve the problem of simplifying 3D point clouds and matching 3D objects. In addition, we show the robustness and efficiency of our proposed methods through different experimental results. Finally, we show the stability and robustness of our methods with respect to noise
Primet, Maël. « Méthodes probabiliste pour le suivi de points et l'analyse d'images biologiques ». Phd thesis, Université René Descartes - Paris V, 2011. http://tel.archives-ouvertes.fr/tel-00669220.
Texte intégralJacob, Christel. « Étude de l’effet structurant des éléments d’un jardin thérapeutique sur la navigation dans la maladie d’Alzheimer : apprentissage de trajet et acquisition des connaissances spatiales ». Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0378.
Texte intégralThe originality of this research is to focus on the characteristics of the physical environment and their impact on navigation and spatial memory capabilities. This field, until then little investigated, represents a societal stake for the autonomy and the well-being of the person. In particular, navigation difficulties have been described in normal aging and Alzheimer's disease (AD) in the early stage. However, the physical environment can support individuals' spatial abilities/skills, or, on the contrary, disrupt them.The aim of the present study is to assess the structuring effect of the elements of a real environment rich in landmarks, the “art, memory and life” healing garden of the CHRU of Nancy, on route learning, and on the acquisition of the spatial knowledge, in a population of subjects with AD. Indeed, the spatial organization of this garden has been designed to contribute, among other things, to alleviate the difficulties of these people in terms of spatial cognition.All the elements of the garden have been listed and integrated into a classification inspired by the works of Lynch (1960) and Zeisel and Tyson (1999). Thirty subjects with mild to moderate AD and 30 matched healthy subjects underwent the following protocol: (1) route learning (forward and return trips), during which the verbal description of the route was recorded; (2) a series of tasks assessing the acquisition of spatial knowledge of the garden as well as (3) standard cognitive tests. The speech was transcribed verbatim and subjected to a content analysis.The results show a significant residual route learning ability in the MA group, both on the forward and return trips. The repetition of the route and the richness of the environmental landmarks seem to have contributed to this result. Experimental task performances were cross-checked with discourse analysis and standard cognitive tests. The results highlight a preponderant role of certain characteristics of environmental elements, such as saliency and affordance, on the learning performances of route and spatial memory, and this even more markedly in the subjects of the MA group.The structuring effect of the elements of the environment is discussed on the one hand in healthy older subjects with regard to cognitive processes involved in the navigation and acquisition of spatial knowledge and on the other hand preserved and dysfunctional processes in the course of Alzheimer’s disease
Destrez, Raphaël. « Recalage automatique de modèles 3D d'arcades dentaires à partir de photographies ». Phd thesis, Université d'Orléans, 2013. http://tel.archives-ouvertes.fr/tel-00994596.
Texte intégralMariadassou, Mahendra. « Robustesse des arbres phylogénétiques ». Phd thesis, Université Paris Sud - Paris XI, 2009. http://tel.archives-ouvertes.fr/tel-00472052.
Texte intégralOukacine, Farid. « Nouvelle méthodologie analytique pour l'étude de l'activité antibactérienne des dendrimères greffés de la L-lysine par électrophorèse capillaire ». Thesis, Montpellier 2, 2011. http://www.theses.fr/2011MON20089/document.
Texte intégralIn this work, a new analytical methodology has been implemented for the screening of antibacterial activity of dendrigraft poly-L-lysines (DGL) by capillary electrophoresis (CE). The principle of this methodology is based on the monitoring of the electrophoretic profile of bacteria before and after the meeting with a zone containing the cationic compound to be screened. The implementation of this methodology has required several steps. In a first experimental part, a new methodology has been developed for the focalization, mobilization and quantification of bacteria. This focusing mode has been applied for the quantification of bacteria in natural waters. In a second experimental part, several neutral capillary coatings were compared for the simultaneous CE analysis of polyanionic and polycationic compounds. In the last experimental part, the screening of antibacterial activity has been implemented on DGL
Doré, Fanny. « Convergences de structures linéaires dans les images : modélisation stochastique et applications en imagerie médicale ». Phd thesis, Université René Descartes - Paris V, 2014. http://tel.archives-ouvertes.fr/tel-01062135.
Texte intégralLiu, Xiang. « Transistor silicium en couche mince à base de nano-particules de PbS : un efficace phototransistor pour la détection de lumière infrarouge ». Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S075/document.
Texte intégralPhototransistor is a novel type of photodetector with special MOSFET structure which can not only convert absorbed light into variation of current but also self-amplify this photocurrent. Especially, with continual advances in quantum dots' (QDs) synthesis, the unique optical-electrical characters reinforce absorption coefficient and electron-hole's generation by easy integrated processes. In this thesis, the infrared PbS QDs with wide infrared (IR) absorption (600-1400 nm) and high efficiency were synthesized to be blended with SU8 gate insulator of Low-Temperature-Poly-Silicon (LTPS) TFTs. Through using this hybrid photo-sensing gate insulator, this LTPS TFTs can still obtain excellent electrical performance such as enough mobility (3.1 cm2/Vs), stable TFT's characters, reasonable on/off ratio (104~105) and subthreshold voltage (3.2 V/Dec). Moreover, under incident IR light's exposure, the high responsivity (1800 A/W) and not negligible responsivity (13 A/W) can be found at 760 nm and 1300 nm respectively. In addition, the photosensitivity also reaches up to 80 and the response time is approximately 30 ms during a pulsed IR signal's scanning. It takes concrete steps forward for the broad application of IR phototransistor
Le, Van Linh. « Automatic landmarking for 2D biological images : image processing with and without deep learning methods ». Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0238.
Texte intégralLandmarks are presented in the applications of different domains such as biomedical or biological. It is also one of the data types which have been usedin different analysis, for example, they are not only used for measuring the form of the object, but also for determining the similarity between two objects. In biology, landmarks are used to analyze the inter-organisms variations, however the supply of landmarks is very heavy and most often they are provided manually. In recent years, several methods have been proposed to automatically predict landmarks, but it is existing the hardness because these methods focused on the specific data. This thesis focuses on automatic determination of landmarks on biological images, more specifically on two-dimensional images of beetles. In our research, we have collaborated with biologists to build a dataset including the images of 293 beetles. For each beetle in this dataset, 5 images correspond to 5 parts have been taken into account, e.g., head, body, pronotum, left and right mandible. Along with each image, a set of landmarks has been manually proposed by biologists. First step, we have brought a method whichwas applied on fly wings, to apply on our dataset with the aim to test the suitability of image processing techniques on our problem. Secondly, we have developed a method consisting of several stages to automatically provide the landmarks on the images.These two first steps have been done on the mandible images which are considered as obvious to use the image processing methods. Thirdly, we have continued to consider other complex remaining parts of beetles. Accordingly, we have used the help of Deep Learning. We have designed a new model of Convolutional Neural Network, named EB-Net, to predict the landmarks on remaining images. In addition, we have proposed a new procedure to augment the number of images in our dataset, which is seen as our limitation to apply deep learning. Finally, to improve the quality of predicted coordinates, we have employed Transfer Learning, another technique of Deep Learning. In order to do that, we trained EB-Net on a public facial keypoints. Then, they were transferred to fine-tuning on beetle’s images. The obtained results have been discussed with biologists, and they have confirmed that the quality of predicted landmarks is statistically good enough to replace the manual landmarks for most of the different morphometry analysis
Fond, Antoine. « Localisation par l'image en milieu urbain : application à la réalité augmentée ». Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0028/document.
Texte intégralThis thesis addresses the problem of localization in urban areas. Inferring accurate positioning in the city is important in many applications such as augmented reality or mobile robotics. However, systems based on inertial sensors (IMUs) are subject to significant drifts and GPS data can suffer from a valley effect that limits their accuracy. A natural solution is to rely on the camera pose estimation in computer vision. We notice that buildings are the main visual landmarks of human beings but also objects of interest for augmented reality applications. We therefore aim to compute the camera pose relatively to a database of known reference buildings from a single image. The problem is twofold : find the visible references in the current image (place recognition) and compute the camera pose relatively to them. Conventional approaches to these two sub-problems are challenged in urban environments due to strong perspective effects, frequent repetitions and visual similarity between facades. While specific approaches to these environments have been developed that exploit the high structural regularity of such environments, they still suffer from a number of limitations in terms of detection and recognition of facades as well as pose computation through model registration. The original method developed in this thesis is part of these specific approaches and aims to overcome these limitations in terms of effectiveness and robustness to clutter and changes of viewpoints and illumination. For do so, the main idea is to take advantage of recent advances in deep learning by convolutional neural networks to extract high-level information on which geometric models can be based. Our approach is thus mixed Bottom- Up/Top-Down and is divided into three key stages. We first propose a method to estimate the rotation of the camera pose. The 3 main vanishing points of the image of urban environnement, known as Manhattan vanishing points, are detected by a convolutional neural network (CNN) that estimates both these vanishing points and the image segmentation relative to them. A second refinement step uses this information and image segmentation in a Bayesian model to estimate these points effectively and more accurately. By estimating the camera’s rotation, the images can be rectified and thus free from perspective effects to find the translation. In a second contribution, we aim to detect the facades in these rectified images to recognize them among a database of known buildings and estimate a rough translation. For the sake of efficiency, a series of cues based on facade specific characteristics (repetitions, symmetry, semantics) have been proposed to enable the fast selection of facade proposals. Then they are classified as facade or non-facade according to a new contextual CNN descriptor. Finally, the matching of the detected facades to the references is done by a nearest neighbor search using a metric learned on these descriptors. Eventually we propose a method to refine the estimation of the translation relying on the semantic segmentation inferred by a CNN for its robustness to changes of illumination ans small deformations. If we can already estimate a rough translation from these detected facades, we choose to refine this result by relying on the se- mantic segmentation of the image inferred from a CNN for its robustness to changes of illuminations and small deformations. Since the facade is identified in the previous step, we adopt a model-based approach by registration. Since the problems of registration and segmentation are linked, a Bayesian model is proposed which enables both problems to be jointly solved. This joint processing improves the results of registration and segmentation while remaining efficient in terms of computation time. These three parts have been validated on consistent community data sets. The results show that our approach is fast and more robust to changes in shooting conditions than previous methods