Auswahl der wissenschaftlichen Literatur zum Thema „Régression de coordonnées de scène“
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Zeitschriftenartikel zum Thema "Régression de coordonnées de scène"
Miñano Martínez, Evelio. „Matei Visniec: aproximación a un universo dramático a través de sus animales“. Çédille 10 (01.04.2014): 255. http://dx.doi.org/10.21071/ced.v10i.5563.
Der volle Inhalt der QuelleVitez, Primož. „L'invention du texte didascalique“. Linguistica 48, Nr. 1 (29.12.2008): 83–94. http://dx.doi.org/10.4312/linguistica.48.1.83-94.
Der volle Inhalt der QuelleBousquet, Marie-Pierre. „Aiamie1, agir au mieux ? Éthique, rituels catholiques et corps social chez les Anicinabek depuis les années 1950“. Hors-thème 20, Nr. 1-2 (16.10.2013): 385–417. http://dx.doi.org/10.7202/1018865ar.
Der volle Inhalt der QuelleCorin, Ellen. „Sous le prisme de la terreur, le travail de la culture“. Anthropologie et Sociétés 32, Nr. 3 (20.04.2009): 57–80. http://dx.doi.org/10.7202/029716ar.
Der volle Inhalt der QuelleLaniel, Marie. „« It was land merely, no land in particular » : le dépaysement à l’œuvre dans Between the Acts (1941) de Virginia Woolf“. Textures, Nr. 24-25 (01.01.2018): 107–19. http://dx.doi.org/10.35562/textures.252.
Der volle Inhalt der QuelleKovacs, Kelly. „La femme dans les films français Dans la maison et Bleu: Une régression“. Inquiry@Queen's Undergraduate Research Conference Proceedings, 20.02.2018. http://dx.doi.org/10.24908/iqurcp.9705.
Der volle Inhalt der QuelleJuvigné, Étienne, Geoffrey Houbrechts und Jean Van Campenhout. „De l’Ourthe primitive à la Meuse primitive en Basse-Meuse liégeoise“. Bulletin de la Société Royale des Sciences de Liège, 2021, 288–316. http://dx.doi.org/10.25518/0037-9565.10603.
Der volle Inhalt der QuelleDissertationen zum Thema "Régression de coordonnées de scène"
Martin-Lac, Victor. „Aerial navigation based on SAR imaging and reference geospatial data“. Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0400.
Der volle Inhalt der QuelleWe seek the algorithmic means of determining the kinematic state of an aerial device from an observation SAR image and reference geospatial data that may be SAR, optical or vector. We determine a transform that relates the observation and reference coordinates and whose parameters are the kinematic state. We follow three approaches. The first one is based on detecting and matching structures such as contours. We propose an iterative closest point algorithm and demonstrate how it can serve to estimate the full kinematic state. We then propose a complete pipeline that includes a learned multimodal contour detector. The second approach is based on a multimodal similarity metric, which is the means of measuring the likelihood that two local patches of geospatial data represent the same geographic point. We determine the kinematic state under the hypothesis of which the SAR image is most similar to the reference geospatial data. The third approach is based on scene coordinates regression. We predict the geographic coordinates of random image patches and infer the kinematic state from these predicted correspondences. However, in this approach, we do not address the fact that the modality of the observation and the reference are different
Duong, Nam duong. „Hybrid Machine Learning and Geometric Approaches for Single RGB Camera Relocalization“. Thesis, CentraleSupélec, 2019. http://www.theses.fr/2019CSUP0008.
Der volle Inhalt der QuelleIn the last few years, image-based camera relocalization becomes an important issue of computer vision applied to augmented reality, robotics as well as autonomous vehicles. Camera relocalization refers to the problematic of the camera pose estimation including both 3D translation and 3D rotation. In localization systems, camera relocalization component is necessary to retrieve camera pose after tracking lost, rather than restarting the localization from scratch.This thesis aims at improving the performance of camera relocalization in terms of both runtime and accuracy as well as handling challenges of camera relocalization in dynamic environments. We present camera pose estimation based on combining multi-patch pose regression to overcome the uncertainty of end-to-end deep learning methods. To balance between accuracy and computational time of camera relocalization from a single RGB image, we propose a sparse feature hybrid methods. A better prediction in the machine learning part of our methods leads to a rapid inference of camera pose in the geometric part. To tackle the challenge of dynamic environments, we propose an adaptive regression forest algorithm that adapts itself in real time to predictive model. It evolves by part over time without requirement of re-training the whole model from scratch. When applying this algorithm to our real-time and accurate camera relocalization, we can cope with dynamic environments, especially moving objects. The experiments proves the efficiency of our proposed methods. Our method achieves results as accurate as the best state-of-the-art methods on the rigid scenes dataset. Moreover, we also obtain high accuracy even on the dynamic scenes dataset
Huynh, Bao Tuyen. „Estimation and feature selection in high-dimensional mixtures-of-experts models“. Thesis, Normandie, 2019. http://www.theses.fr/2019NORMC237.
Der volle Inhalt der QuelleThis thesis deals with the problem of modeling and estimation of high-dimensional MoE models, towards effective density estimation, prediction and clustering of such heterogeneous and high-dimensional data. We propose new strategies based on regularized maximum-likelihood estimation (MLE) of MoE models to overcome the limitations of standard methods, including MLE estimation with Expectation-Maximization (EM) algorithms, and to simultaneously perform feature selection so that sparse models are encouraged in such a high-dimensional setting. We first introduce a mixture-of-experts’ parameter estimation and variable selection methodology, based on l1 (lasso) regularizations and the EM framework, for regression and clustering suited to high-dimensional contexts. Then, we extend the method to regularized mixture of experts models for discrete data, including classification. We develop efficient algorithms to maximize the proposed l1 -penalized observed-data log-likelihood function. Our proposed strategies enjoy the efficient monotone maximization of the optimized criterion, and unlike previous approaches, they do not rely on approximations on the penalty functions, avoid matrix inversion, and exploit the efficiency of the coordinate ascent algorithm, particularly within the proximal Newton-based approach