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Добірка наукової літератури з теми "Caractéristiques spatio-Temporelles"
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Статті в журналах з теми "Caractéristiques spatio-Temporelles"
Ledent, Jacques. "Une analyse log-linéaire des courants migratoires interprovinciaux : Canada, 1961-1983." Cahiers québécois de démographie 12, no. 2 (October 27, 2008): 223–50. http://dx.doi.org/10.7202/600508ar.
Повний текст джерелаSick, Lisa, Albin Ullmann, and Pascal Roucou. "La rupture climatique de 1987 en France : quels effets sur l’humidité des sols ?" Climatologie 18 (2021): 5. http://dx.doi.org/10.1051/climat/202118005.
Повний текст джерелаMauti, Rafael, RomainTisserand, Suliann Ben Hamed, Thomas Robert, Laurence Cheze, and Pascal Chabaud. "Inhiber l’initiation d’un pas : effets du contexte d’inhibition sur les caractéristiques spatio-temporelles des ajustements posturaux anticipés (APA)." Neurophysiologie Clinique 49, no. 6 (December 2019): 426. http://dx.doi.org/10.1016/j.neucli.2019.10.055.
Повний текст джерелаMarty, C., and E. Beall. "Modalités spatio-temporelles de la dispersion d'alevins de saumon atlantique (Salmo salar L.) à l'émergence." Revue des sciences de l'eau 2, no. 4 (April 12, 2005): 831–46. http://dx.doi.org/10.7202/705057ar.
Повний текст джерелаFagani, Jeanne. "Organisation de l’espace et activité professionnelle des mères : le cas des nouvelles couches moyennes en région Île-de-France." Cahiers de géographie du Québec 31, no. 83 (April 12, 2005): 225–36. http://dx.doi.org/10.7202/021877ar.
Повний текст джерелаNola, M., T. Njine, V. F. Sikati, and E. Djuikom. "Distribution de Pseudomonas aeruginosa et Aeromonas hydrophila dans les eaux de la nappe phréatique superficielle en zone équatoriale au Cameroun et relations avec quelques paramètres chimiques du milieu." Revue des sciences de l'eau 14, no. 1 (April 12, 2005): 35–53. http://dx.doi.org/10.7202/705407ar.
Повний текст джерелаLauriol, Bernard, André Champoux, and James T. Gray. "Répartition estivale des surfaces enneigées en Ungava, Nouveau-Québec." Géographie physique et Quaternaire 38, no. 1 (November 29, 2007): 37–47. http://dx.doi.org/10.7202/032534ar.
Повний текст джерелаKemka, N., T. Njine, S. H. Zébazé Togouet, D. Niyitegeka, M. Nola, A. Monkiedje, J. Demannou, and S. Foto Menbohan. "Phytoplancton du lac municipal de Yaoundé (Cameroun) : Succession écologique et structure des peuplements." Revue des sciences de l'eau 17, no. 3 (April 12, 2005): 301–16. http://dx.doi.org/10.7202/705535ar.
Повний текст джерелаEtilé*, Raphaël N’doua, Marius Tanoh Kamelan, Théophile Aké Bedia, Maryse N’guessan Aka, Gouli Gooré Bi, Paul Essetchi Kouamelan, and Valentin N’douba. "Variations spatio-temporelles de l’abondance des nauplii de copépodes dans les lagunes côtières tropicales en relation avec les variables environnementales : cas des lagunes de la Côte d’Ivoire." Tropicultura, no. 3-4 (2020). http://dx.doi.org/10.25518/2295-8010.1601.
Повний текст джерелаGaspar, Thomas, Claude Penel, and Hubert Greppin. "Approche analogique et réalités des phytohormones : des retards et des erreurs stratégiques?" Bulletin de la Société Royale des Sciences de Liège, 2016, 190–208. http://dx.doi.org/10.25518/0037-9565.6236.
Повний текст джерелаДисертації з теми "Caractéristiques spatio-Temporelles"
Baccouche, Moez. "Apprentissage neuronal de caractéristiques spatio-temporelles pour la classification automatique de séquences vidéo." Phd thesis, INSA de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-00932662.
Повний текст джерелаOuergli, Abdelkader. "Caractéristiques spatio-temporelles des ondes 10-20 et 25-50 jours pendant la mousson indienne d'été." Clermont-Ferrand 2, 1993. http://www.theses.fr/1993CLF21496.
Повний текст джерелаFouillet, Anne. "Surmortalité liée aux vagues de chaleur : modélisation des variations spatio-temporelles de la mortalité générale en fonction des caractéristiques climatiques." Paris 11, 2007. http://www.theses.fr/2007PA11T031.
Повний текст джерелаChelali, Mohamed Tayeb. "Prise en compte de l'information spatiale et temporelle pour l'analyse de séquences d'images." Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5205.
Повний текст джерелаThe evolution of digital technology has allowed the multiplicity of image sensors, leading every day to the production of masses of visual data. In some contexts, these data can take the form of 2D images time series leading to 3D data that we note 2D+t. This type of data is frequent in several domains such as remote surveillance or remote sensing. Because of their dimensions, the analysis and interpretation of this mass of data is a major challenge in computer vision. This thesis is in the context of the exploitation of these data in order to classify them, by exploiting the maximum the wealth of spatial and temporal information carried by these data. The research works presented in this manuscript includes two methods that proceed differently but whose common point is based on a change of the representation of the initial data. The first method is based on the extraction of hand-crafted features while the second one is based on the use of machine learning methods, in particular deep convolutional neural networks. Through these two methods, we propose to study the temporal stability of image times series with hand-crafted features and to study their spatial and temporal variability with deep convolutional neural networks. The two methods are then evaluated on two different applications. One is related to satellite image time series and the other is related to surveillance camera videos. The experimental results illustrate the interest of the proposed methods
Shao, Wenhao. "Enhancing Video Anomaly Detection by Leveraging Advanced Deep Learning Techniques." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS012.
Повний текст джерелаSecurity in public spaces is a primary concern across different domains and the deployment of real-time monitoring systems addresses this challenge. Video surveillance systems employing deep learning techniques allows for the effective recognition of anomaly events. However, even with the current advances in anomaly detection methods, distinguishing abnormal events from normal events in real-world scenarios remains a challenge because they often involve rare, visually diverse, and unrecognizable abnormal events. This is particularly true when relying on supervised methods, where the lack of sufficient labeled anomaly data poses a significant challenge for distinguishing between normal and abnormal videos. As a result, state-of-the-art anomaly detection approaches utilize existing datasets to design or learn a model that captures normal patterns, which is then helpful in identifying unknown abnormal patterns. During the model design stage, it is crucial to label videos with attributes such as abnormal appearance, behavior, or target categories that deviate significantly from normal data, marking them as anomalies. In addition to the lack of labeled data, we identified three challenges from the literature: 1) insufficient representation of temporal feature, 2) lack of precise positioning of abnormal events and 3) lack the consistency research of temporal feature and appearance feature. The objective of my thesis is to propose and investigate advanced video anomaly detection methods by addressing the aforementioned challenges using novel concepts and utilizing weak supervision and unsupervised models rather than relying on supervised models.We actively explored the applications of new video processing technologies, including action recognition, target detection, optical flow feature extraction, representation learning, and contrastive learning in order to utilize them in video anomaly detection models. Our proposed models comparatively analysed with baseline models. This comparative analysis are conducted using prevalent public datasets, including UCSD(Ped2), Avenue, UCF-Crime, and Shanghaitech.The first contribution addresses the first challenge outlined above by introducing an enhanced Temporal Convolutional Network (TCN). This novel TCN model learns dynamic video features and optimizes features to mitigate errors due to contrastive learned initial weights. This method enhances the overall capability of weakly supervised models by reducing the loss caused by initial parameters in contrastive learning. Nevertheless, weakly supervised learning only reduces the reliance on labeled data but does not eliminate the dependence on such data. Hence, our subsequent two contributions rely on unsupervised learning to addressing the other two challenges mentioned above. The second contribution combines the self-attention mechanism to prioritize the weights of areas with obvious dynamic fluctuations in frames. And, during the testing, abnormal areas are located through comparison of object detection and loss functions. The combination of self-attention mechanism and object detection significantly improves the detection accuracy and expands the functionality. The third contribution explores the integration of collaborative teaching network models, which bridges consistency between optical flow information and appearance information. This integration aims to enhance the spatio-temporal capture capabilities of unsupervised models. The overall performance and capabilities of the unsupervised model are significantly enhanced compared to the other baseline models
Lenoir, Jean-Michel. "Temps de cohérence temporelle de structures turbulentes porteuses de scalaires passifs au sein d'une turbulence homogène quasi-isotrope." Phd thesis, Université Claude Bernard - Lyon I, 2011. http://tel.archives-ouvertes.fr/tel-00819861.
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