Relevant bibliographies by topics / Deep Learning Fusion / Dissertations / Theses
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Dissertations / Theses on the topic 'Deep Learning Fusion'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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1

Howard, Shaun Michael. "Deep Learning for Sensor Fusion." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1495751146601099.

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2

Nguyen, Tien Dung. "Multimodal emotion recognition using deep learning techniques." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/180753/1/Tien%20Dung_Nguyen_Thesis.pdf.

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This thesis investigates the use of deep learning techniques to address the problem of machine understanding of human affective behaviour and improve the accuracy of both unimodal and multimodal human emotion recognition. The objective was to explore how best to configure deep learning networks to capture individually and jointly, the key features contributing to human emotions from three modalities (speech, face, and bodily movements) to accurately classify the expressed human emotion. The outcome of the research should be useful for several applications including the design of social robots.
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Andrade, Valente da Silva Michelle. "SLAM and data fusion for autonomous vehicles : from classical approaches to deep learning methods." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEM079.

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L'arrivée des voitures autonomes va provoquer une transformation très importante de la mobilité urbaine telle que nous la connaissons, avec un impact significatif sur notre vie quotidienne. En effet, elles proposent un nouveau système de déplacement plus efficace, plus facilement accessible et avec une meilleure sécurité routière. Pour atteindre cet objectif, les véhicules autonomes doivent effectuer en toute sécurité et de manière autonome trois tâches principales: la perception, la planification et le contrôle. La perception est une tâche particulièrement difficile en milieu urbain, car elle
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Bodén, Johan. "A Comparative Study of Reinforcement-­based and Semi­-classical Learning in Sensor Fusion." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-84784.

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Reinforcement learning has proven itself very useful in certain areas, such as games. However, the approach has been seen as quite limited. Reinforcement-based learning has for instance not been commonly used for classification tasks as it is receiving feedback on how well it did for an action performed on a specific input. This slows the performance convergence rate as compared to other classification approaches which has the input and the corresponding output to train on. Nevertheless, this thesis aims to investigate whether reinforcement-based learning could successfully be employed on a cl
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Abd, Gaus Yona Falinie. "Artificial intelligence system for continuous affect estimation from naturalistic human expressions." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/16348.

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The analysis and automatic affect estimation system from human expression has been acknowledged as an active research topic in computer vision community. Most reported affect recognition systems, however, only consider subjects performing well-defined acted expression, in a very controlled condition, so they are not robust enough for real-life recognition tasks with subject variation, acoustic surrounding and illumination change. In this thesis, an artificial intelligence system is proposed to continuously (represented along a continuum e.g., from -1 to +1) estimate affect behaviour in terms o
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Baier, Stephan [Verfasser], and Volker [Akademischer Betreuer] Tresp. "Learning representations for supervised information fusion using tensor decompositions and deep learning methods / Stephan Baier ; Betreuer: Volker Tresp." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2019. http://d-nb.info/1185979220/34.

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TOOSI, AMIRHOSEIN. "Feature Fusion for Fingerprint Liveness Detection." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2711594.

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For decades, fingerprints have been the most widely used biometric trait in identity recognition systems, thanks to their natural uniqueness, even in rare cases such as identical twins. Recently, we witnessed a growth in the use of fingerprint-based recognition systems in a large variety of devices and applications. This, as a consequence, increased the benefits for offenders capable of attacking these systems. One of the main issues with the current fingerprint authentication systems is that, even though they are quite accurate in terms of identity verification, they can be easily spoo
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Sha, Mingzhi. "A Novel Semantic Feature Fusion-based Pedestrian Detection System to Support Autonomous Vehicles." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42213.

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Intelligent transportation systems (ITS) have become a popular method to enhance the safety and efficiency of transportation. Pedestrians, as an essential participant of ITS, are very vulnerable in a traffic collision, compared with the passengers inside the vehicle. In order to protect the safety of all traffic participants and enhance transportation efficiency, the novel autonomous vehicles are required to detect pedestrians accurately and timely. In the area of pedestrian detection, deep learning-based pedestrian detection methods have gained significant development since the appearance
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Praboda, Chathurangani Rajapaksha Rajapaksha Waththe Vidanelage. "Clickbait detection using multimodel fusion and transfer learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAS025.

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Presque tous les internautes sont susceptibles d'être victimes de clickbait, supposant à tort qu’il s’agit d’informations légitimes. Un type important de clickbait se présente sous la forme de spam et de publicités qui sont utilisés pour rediriger les utilisateurs vers des sites web. Un autre type de "clickbait" est conçu pour faire la une des journaux et rediriger les lecteurs vers leurs sites en ligne, mais ces nouvelles sensationnelles peuvent être trompeuses. Il est difficile de prédire le degré de click-baity d'une nouvelle donnée car les clickbait sont des messages très courts et écrits
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Stone, David L. "The Application of Index Based, Region Segmentation, and Deep Learning Approaches to Sensor Fusion for Vegetation Detection." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5708.

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This thesis investigates the application of index based, region segmentation, and deep learning methods to the sensor fusion of omnidirectional (O-D) Infrared (IR) sensors, Kinnect sensors, and O-D vision sensors to increase the level of intelligent perception for unmanned robotic platforms. The goals of this work is first to provide a more robust calibration approach and improve the calibration of low resolution and noisy IR O-D cameras. Then our goal was to explore the best approach to sensor fusion for vegetation detection. We looked at index based, region segmentation, and deep learning me
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Helvig, Kevin. "Apprentissage multi-capteurs pour le contrôle non destructif de matériaux." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG079.

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La détection de défauts dans les structures aéronautiques et spatiales est cruciale, tant pour la fabrication que pour la maintenance. Les méthodes de contrôle non-destructif doivent être rapides, précises, fiables, économiques et de plus en plus automatisées. La complémentarité des différentes techniques d'inspection suggère leur utilisation simultanée pour renforcer la fiabilité des informations ou permettre une détection automatique difficile avec une seule technique. Dans ce travail de thèse, nous explorons l'utilisation des méthodes d'apprentissage profond et de synthèse d'images pour la
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Hu, Sijie. "Deep multimodal visual data fusion for outdoor scenes analysis in challenging weather conditions." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPAST121.

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Les données visuelles multimodales peuvent fournir des informations différentes sur la même scène, améliorant ainsi la précision et la robustesse de l'analyse de scènes. Cette thèse se concentre principalement sur la façon d'utiliser efficacement les données visuelles multimodales telles que les images en couleur, les images infrarouges et les images de profondeur, et sur la façon de fusionner ces données visuelles pour une compréhension plus complète de l'environnement. Nous avons choisi la segmentation sémantique et la détection d'objets, deux tâches représentatives de la vision par ordinate
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Minto, Ludovico. "Deep learning for scene understanding with color and depth data." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3422424.

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Significant advancements have been made in the recent years concerning both data acquisition and processing hardware, as well as optimization and machine learning techniques. On one hand, the introduction of depth sensors in the consumer market has made possible the acquisition of 3D data at a very low cost, allowing to overcome many of the limitations and ambiguities that typically affect computer vision applications based on color information. At the same time, computationally faster GPUs have allowed researchers to perform time-consuming experimentations even on big data. On the other ha
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Slizovskaia, Olga. "Audio-visual deep learning methods for musical instrument classification and separation." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/669963.

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In music perception, the information we receive from a visual system and audio system is often complementary. Moreover, visual perception plays an important role in the overall experience of being exposed to a music performance. This fact brings attention to machine learning methods that could combine audio and visual information for automatic music analysis. This thesis addresses two research problems: instrument classification and source separation in the context of music performance videos. A multimodal approach for each task is developed using deep learning techniques to train an encoded
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Stanislas, Leo. "Detecting airborne particles in sensor data with deep learning for robust robot perception in adverse environments." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/200382/1/Leo_Stanislas_Thesis.pdf.

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This thesis presents a novel method to detect airborne particles such as dust, fog, or smoke, in the data from LiDAR sensors and stereo cameras, two types of perception sensors commonly used in robotics. The proposed approach uses deep learning classification and stochastic data fusion to detect and correctly interpret sensor data points impacted by airborne particles. The work from this thesis will enable robots to reliably perform complex tasks in challenging and unpredictable environments such as mines, agricultural fields, or roads, including in adverse weather conditions.
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MONTEMURRO, MARILISA. "Algorithms for cancer genome data analysis - Learning techniques for ITH modeling and gene fusion classification." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2970978.

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17

Lundberg, Gustav. "Automatic map generation from nation-wide data sources using deep learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170759.

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The last decade has seen great advances within the field of artificial intelligence. One of the most noteworthy areas is that of deep learning, which is nowadays used in everything from self driving cars to automated cancer screening. During the same time, the amount of spatial data encompassing not only two but three dimensions has also grown and whole cities and countries are being scanned. Combining these two technological advances enables the creation of detailed maps with a multitude of applications, civilian as well as military.This thesis aims at combining two data sources covering most
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Cherif, Mohamed Abderrazak. "Alignement et fusion de cartes géospatiales multimodales hétérogènes." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ5002.

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L'augmentation des données dans divers domaines présente un besoin essentiel de techniques avancées pour fusionner et interpréter ces informations. Avec une emphase particulière sur la compilation de données géospatiales, cette intégration est cruciale pour débloquer de nouvelles perspectives à partir des données géographiques, améliorant notre capacité à cartographier et analyser les tendances qui s'étendent à travers différents lieux et environnements avec plus d'authenticité et de fiabilité. Les techniques existantes ont progressé dans l'adresse de la fusion des données ; cependant, des déf
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He, Linbo. "Improving 3D Point Cloud Segmentation Using Multimodal Fusion of Projected 2D Imagery Data : Improving 3D Point Cloud Segmentation Using Multimodal Fusion of Projected 2D Imagery Data." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157705.

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Semantic segmentation is a key approach to comprehensive image data analysis. It can be applied to analyze 2D images, videos, and even point clouds that contain 3D data points. On the first two problems, CNNs have achieved remarkable progress, but on point cloud segmentation, the results are less satisfactory due to challenges such as limited memory resource and difficulties in 3D point annotation. One of the research studies carried out by the Computer Vision Lab at Linköping University was aiming to ease the semantic segmentation of 3D point cloud. The idea is that by first projecting 3D dat
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Salehi, Achkan. "Localisation précise d'un véhicule par couplage vision/capteurs embarqués/systèmes d'informations géographiques." Thesis, Université Clermont Auvergne‎ (2017-2020), 2018. http://www.theses.fr/2018CLFAC064/document.

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La fusion entre un ensemble de capteurs et de bases de données dont les erreurs sont indépendantes est aujourd’hui la solution la plus fiable et donc la plus répandue de l’état de l’art au problème de la localisation. Les véhicules semi-autonomes et autonomes actuels, ainsi que les applications de réalité augmentée visant les contextes industriels exploitent des graphes de capteurs et de bases de données de tailles considérables, dont la conception, la calibration et la synchronisation n’est, en plus d’être onéreuse, pas triviale. Il est donc important afin de pouvoir démocratiser ces technolo
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Warnakulasuriya, Tharindu R. "Context modelling for single and multi agent trajectory prediction." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/128480/1/Tharindu_Warnakulasuriya_Thesis.pdf.

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This research addresses the problem of predicting future agent behaviour in both single and multi agent settings where multiple agents can enter and exit an environment, and the environment can change dynamically. Both short-term and long-term context was captured in the given domain and utilised neural memory networks to use the derived knowledge for the prediction task. The efficacy of the techniques was demonstrated by applying it to aircraft path prediction, passenger movement prediction in crowded railway stations, driverless car steering, predicting next shot location in tennis and for p
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Zhang, Yifei. "Real-time multimodal semantic scene understanding for autonomous UGV navigation." Thesis, Bourgogne Franche-Comté, 2021. http://www.theses.fr/2021UBFCK002.

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Une analyse sémantique robuste des scènes extérieures est difficile en raison des changements environnementaux causés par l'éclairage et les conditions météorologiques variables, ainsi que par la variation des types d'objets rencontrés. Cette thèse étudie le problème de la segmentation sémantique à l'aide de l'apprentissage profond et avec des d'images de différentes modalités. Les images capturées à partir de diverses modalités d'acquisition fournissent des informations complémentaires pour une compréhension complète de la scène. Nous proposons des solutions efficaces pour la segmentation sup
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Chowdhury, Alok K. "Sensor-based prediction of physical activity and its impacts using machine learning." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/118664/1/Alok_Chowdhury_Thesis.pdf.

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This thesis contributed to the development of advanced learning models and multi-sensor decision fusion algorithm to improve the prediction of physical activity and its personal impacts including relative intensity and energy expenditure from wearable sensor data. It identified the optimal sensor positioning and optimal combination of multimodal sensor data for assessing physical activity and predicting its impacts. All methods of this thesis collectively deliver better algorithms and maximise the use of available sensor information to provide accurate measurement of physical activity.
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Ghosh, Binayak. "Opto-Acoustic Slopping Prediction System in Basic Oxygen Furnace Converters." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219614.

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Today, everyday objects are becoming more and more intelligent and some-times even have self-learning capabilities. These self-learning capacities in particular also act as catalysts for new developments in the steel industry.Technical developments that enhance the sustainability and productivity of steel production are very much in demand in the long-term. The methods of Industry 4.0 can support the steel production process in a way that enables steel to be produced in a more cost-effective and environmentally friendly manner. This thesis describes the development of an opto-acoustic system f
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MAGGIOLO, LUCA. "Deep Learning and Advanced Statistical Methods for Domain Adaptation and Classification of Remote Sensing Images". Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1070050.

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In the recent years, remote sensing has faced a huge evolution. The constantly growing availability of remote sensing data has opened up new opportunities and laid the foundations for many new challenges. The continuous space missions and new constellations of satellites allow in fact more and more frequent acquisitions, at increasingly higher spatial resolutions, and at an almost total coverage of the globe. The availability of such an huge amount data has highlighted the need for automatic techniques capable of processing the data and exploiting all the available information. Meanwhile, the
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Berka, Anas. "Smart farming : Système d’aide à la décision basé sur la fusion de données multi-sources." Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2024. http://www.theses.fr/2024ISAB0012.

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Le travail présenté dans ce manuscrit traite de l'intégration de données multi-sources, dans le cadre applicatif de l'agriculture de précision, en mettant l'accent sur la fusion de données issues de diverses modalités telles que les images satellitaires, aériennes et de proximité. La fusion de ces sources variées vise à exploiter leurs complémentarités pour améliorer la gestion des cultures, notamment dans le cadre de la détection des maladies.Plusieurs approches basées sur l'apprentissage profond ont ainsi été proposées, notamment les architectures Vision Transformers (ViT) et DeepLab, adapté
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Siddiqui, Mohammad Faridul Haque. "A Multi-modal Emotion Recognition Framework Through The Fusion Of Speech With Visible And Infrared Images." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1556459232937498.

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Liu, Li. "Modélisation pour la reconnaissance continue de la langue française parlée complétée à l'aide de méthodes avancées d'apprentissage automatique." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAT057/document.

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Cette thèse de doctorat traite de la reconnaissance automatique du Langage français Parlé Complété (LPC), version française du Cued Speech (CS), à partir de l’image vidéo et sans marquage de l’information préalable à l’enregistrement vidéo. Afin de réaliser cet objectif, nous cherchons à extraire les caractéristiques de haut niveau de trois flux d’information (lèvres, positions de la main et formes), et fusionner ces trois modalités dans une approche optimale pour un système de reconnaissance de LPC robuste. Dans ce travail, nous avons introduit une méthode d’apprentissage profond avec les rés
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Langenberg, Tristan Matthias [Verfasser], Florentin [Akademischer Betreuer] Wörgötter, Florentin [Gutachter] Wörgötter, et al. "Deep Learning Metadata Fusion for Traffic Light to Lane Assignment / Tristan Matthias Langenberg ; Gutachter: Florentin Wörgötter, Carsten Damm, Wolfgang May, Jens Grabowski, Stephan Waack, Minija Tamosiunaite ; Betreuer: Florentin Wörgötter." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2019. http://d-nb.info/1191989100/34.

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Corbat, Lisa. "Fusion de segmentations complémentaires d'images médicales par Intelligence Artificielle et autres méthodes de gestion de conflits." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCD029.

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Le néphroblastome est la tumeur du rein la plus fréquente chez l'enfant et son diagnostic est exclusivement basé sur l'imagerie. Ce travail qui fait l'objet de nos recherches s'inscrit dans le cadre d'un projet de plus grande envergure : le projet européen SAIAD (Segmentation Automatique de reins tumoraux chez l'enfant par Intelligence Artificielle Distribuée). L'objectif du projet est de parvenir à concevoir une plate-forme capable de réaliser différentes segmentations automatiques sur les images sources à partir de méthodes d'Intelligence Artificielle (IA), et ainsi obtenir une reconstructio
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Radhakrishnan, Aswathnarayan. "A Study on Applying Learning Techniques to Remote Sensing Data." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586901481703797.

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Agresti, Gianluca. "Data Driven Approaches for Depth Data Denoising." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3422722.

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The scene depth is an important information that can be used to retrieve the scene geometry, a missing element in standard color images. For this reason, the depth information is usually employed in many applications such as 3D reconstruction, autonomous driving and robotics. The last decade has seen the spread of different commercial devices able to sense the scene depth. Among these, Time-of-Flight (ToF) cameras are becoming popular because they are relatively cheap and they can be miniaturized and implemented on portable devices. Stereo vision systems are the most widespread 3D senso
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Bakkali, Souhail. "Multimodal Document Understanding with Unified Vision and Language Cross-Modal Learning." Electronic Thesis or Diss., La Rochelle, 2022. http://www.theses.fr/2022LAROS046.

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Les modèles développés dans cette thèse sont le résultat d'un processus itératif d'analyse et de synthèse entre les théories existantes et nos études réalisées. Plus spécifiquement, nous souhaitons étudier l'apprentissage inter-modal pour la compréhension contextualisée sur les composants des documents à travers le langage et la vision. Cette thèse porte sur l'avancement de la recherche sur l'apprentissage inter-modal et apporte des contributions sur quatre fronts : (i) proposer une approche inter-modale avec des réseaux profonds pour exploiter conjointement les informations visuelles et textu
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SAVARESE, FRANCESCO. "Data Fusion Methods and Algorithms in the Context of Autonomous Systems - A path planning algorithms analysis and optimization exploiting fused data." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2752655.

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Ouzar, Yassine. "Reconnaissance automatique sans contact de l'état affectif de la personne par fusion physio-visuelle à partir de vidéo du visage." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0076.

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La reconnaissance automatique de l'état affectif reste un sujet difficile en raison de la complexité des émotions / stress, qui impliquent des éléments expérientiels, comportementaux et physiologiques. Comme il est difficile de décrire l'état affectif de la personne de manière exhaustive en termes de modalités uniques, des études récentes se sont concentrées sur des stratégies de fusion afin d'exploiter la complémentarité des signaux multimodaux. L'objectif principal de cette thèse consiste à étudier la faisabilité d'une fusion physio-visuelle pour la reconnaissance automatique de l'état affec
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Capellier, Édouard. "Application of machine learning techniques for evidential 3D perception, in the context of autonomous driving." Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2534.

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L’apprentissage machine a révolutionné la manière dont les problèmes de perception sont, actuellement, traités. En effet, la plupart des approches à l’état de l’art, dans de nombreux domaines de la vision par ordinateur, se reposent sur des réseaux de neurones profonds. Au moment de déployer, d’évaluer, et de fusionner de telles approches au sein de véhicules autonomes, la question de la représentation des connaissances extraites par ces approches se pose. Dans le cadre de ces travaux de thèse, effectués au sein de Renault SAS, nous avons supposé qu’une représentation crédibiliste permettait d
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Harbaoui, Nesrine. "Diagnostic adaptatif à l'environnement de navigation : apport de l'apprentissage profond pour une localisation sûre et précise." Electronic Thesis or Diss., Université de Lille (2022-....), 2022. http://www.theses.fr/2022ULILB041.

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Pour un système de transport terrestre autonome, la capacité de déterminer sa position est essentielle afin de permettre à d'autres fonctions, telles que le contrôle ou la planification de trajectoire, d'être exécutées sans danger. Ainsi, la criticité de ces fonctions génère des exigences importantes en termes de sûreté (intégrité), de disponibilité, de justesse et de précision. Pour les véhicules terrestres, la satisfaction de ces exigences est liée à divers paramètres tels que la dynamique du véhicule, les conditions météorologiques, ou encore le contexte de navigation, qui comprend à la foi
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Osman, Ousama. "Méthodes de diagnostic en ligne, embarqué et distribué dans les réseaux filaires complexes." Thesis, Université Clermont Auvergne‎ (2017-2020), 2020. http://www.theses.fr/2020CLFAC038.

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Les recherches menées dans cette thèse portent sur le diagnostic de réseaux filaires complexes à l’aide de la réflectométrie distribuée. L’objectif est de développer de nouvelles technologies de diagnostic en ligne, distribuées des réseaux complexes permettant la fusion de données ainsi que la communication entre les réflectomètres pour détecter, localiser et caractériser les défauts électriques (francs et non francs). Cette collaboration entre les réflectomètres permet de résoudre le problème d’ambiguïté de localisation des défauts et d’améliorer la qualité du diagnostic. La première contribu
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"Deep Domain Fusion for Adaptive Image Classification." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.55006.

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abstract: Endowing machines with the ability to understand digital images is a critical task for a host of high-impact applications, including pathology detection in radiographic imaging, autonomous vehicles, and assistive technology for the visually impaired. Computer vision systems rely on large corpora of annotated data in order to train task-specific visual recognition models. Despite significant advances made over the past decade, the fact remains collecting and annotating the data needed to successfully train a model is a prohibitively expensive endeavor. Moreover, these models are prone
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Langenberg, Tristan Matthias. "Deep Learning Metadata Fusion for Traffic Light to Lane Assignment." Doctoral thesis, 2019. http://hdl.handle.net/21.11130/00-1735-0000-0003-C184-D.

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"Data-Driven Representation Learning in Multimodal Feature Fusion." Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.50428.

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abstract: Modern machine learning systems leverage data and features from multiple modalities to gain more predictive power. In most scenarios, the modalities are vastly different and the acquired data are heterogeneous in nature. Consequently, building highly effective fusion algorithms is at the core to achieve improved model robustness and inferencing performance. This dissertation focuses on the representation learning approaches as the fusion strategy. Specifically, the objective is to learn the shared latent representation which jointly exploit the structural information encoded in all m
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Wu, Che-Yi, and 吳哲逸. "Image Retrieval with Fusion Descriptor from Deep Learning and Compressed Domain Features." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/6f6992.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>104<br>This thesis presents an effective image retrieval by combining low-level features from Dot-Diffused Block Truncation Coding (DDBTC) and high-level features from Convolutional Neural Network (CNN) model. The low-level features are constructed by the proposed two-layer codebook feature from DDBTC bitmap, maximum, and minimum quantizers. The two-layer codebook is to improve the limited dimension of original codebook. The high-level feature is from CNN which is a very effective approach for deep learning. The high-level feature has been widely applied in recogniti
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Chen, Yi-Wei, and 陳宜緯. "Diabetic Retinopathy Recognition with Fusion of Supervised Deep Learning Features and Segmented Symptoms." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/b67gzs.

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碩士<br>國立交通大學<br>電子研究所<br>107<br>Diabetic retinopathy is the primary cause of blindness in the working-age population of the developed world. Diagnosing the disease heavily relies on imaging studies, which is a time consuming and a manual process performed by trained clinicians. Enhancing the accuracy and speed of the detection process can potentially have a significant impact on population health via early diagnosis and intervention. Besides the prevention, how to keep tracking the treatment effect for the patient with diabetic retinopathy is another crucial issue in personalized healthcare. M
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Ram, Prabhakar Kathirvel. "Advances in High Dynamic Range Imaging Using Deep Learning." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5515.

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Natural scenes have a wide range of brightness, from dark starry nights to bright sunlit beaches. Our human eyes can perceive such a vast range of illumination through various adaptation techniques, thus allowing us to enjoy them. Contrarily, digital cameras can capture a limited brightness range due to their sensor limitations. Often, the dynamic range of the scene far exceeds the hardware limit of standard digital camera sensors. In such scenarios, the resulting photos will consist of saturated regions, either too dark or too bright to visually comprehend. An easy to deploy and widely used a
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LU, PO-HSUAN, and 盧柏瑄. "Abrasive Grain Diamond Wire Saw Inspection system based on Exposure Fusion and Deep Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/ye6frz.

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碩士<br>國立臺北科技大學<br>製造科技研究所<br>107<br>This thesis develops a set of optical inspection system applying on diamond wire saw, which can test and observe with graphical user interface and acquire key parameters such as abrasive distribution, abrasive protrusion, wire diameter and coating thickness by analyzing collected data. During detecting surface abrasives, the most challenging factor is the surface of the plated diamond wire may have different reflection coefficients in different sections. Moreover, the top of the surface of arc is tend to over-exposed, while the exposure intensity at the wire
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Katare, Dewant. "Exploration of Deep Learning Applications on An Autonomous Embedded Platform (Bluebox 2.0)." Thesis, 2019. http://hdl.handle.net/1805/21462.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>An Autonomous vehicle depends on the combination of latest technology or the ADAS safety features such as Adaptive cruise control (ACC), Autonomous Emergency Braking (AEB), Automatic Parking, Blind Spot Monitor, Forward Collision Warning or Avoidance (FCW or FCA), Lane Departure Warning. The current trend follows incorporation of these technologies using the Artificial neural network or Deep neural network, as an imitation of the traditionally used algorithms. Recent research in the field of deep learning and development of compete
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Chen, Jing-Min, and 陳璟旻. "Fusion of Drone Images and Deep Learning for 3D Object Modeling, View Estimation and Retrieval." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/y4rbu3.

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碩士<br>國立臺灣海洋大學<br>資訊工程學系<br>106<br>A drone with a camera has been widely used through these years due to its functions of high mobility, immediate scanning, and to monitor the environment promptly. With the ability to continuously capture the targeted object, and integrates technologies such as big data prediction analysis, integrated object detection, environmental comparison, or/and object behavior identification, we could build an immediate surveillance system for the need of disaster prevention. Normally, drone photos are 2D image, and we need to navigate a drone to different angles, direc
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Wu, Po-Yu, and 吳柏叡. "Real-time Prediction of Lane Change and Lane Departure Based on Multi-Layer Deep Learning Sensory-fusion." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/v53478.

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碩士<br>元智大學<br>資訊工程學系<br>106<br>According to the statistic, there are 18% car accidents caused by the anomaly lane change. The advanced driver assistance system (ADAS) is employed to mitigate car accident occur effectively, which is the popular area recently. In this study, we propose the Lane Action Prediction System (LAPS) to predict the driver’s behavior that include lane change and lane departure. The LAPS based on a multi-layer deep learning architecture, which consist CNN and bidirectional LSTM algorithm, is usefully for the high dimension and time-series data. In this study, we use camer
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Heydarian, Hamid. "Using deep learning to assess eating behaviours with wrist-worn inertial sensors." Thesis, 2022. http://hdl.handle.net/1959.13/1439012.

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Research Doctorate - Doctor of Philosophy (PhD)<br>In today’s world, cardiovascular diseases such as heart attacks and stoke are the leading type of chronic diseases in terms of premature death. Unhealthy diet is among habits that increase the metabolic risk in an individual (e.g., obesity, increased blood glucose, and raised blood pressure) that may lead to cardiovascular diseases. Therefore, being able to accurately monitor dietary intake activities of individuals could play an important part in promoting a healthier diet and preventing cardiovascular diseases. Wearable motion tracking senso
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Fu, Yu-Ju, and 傅于洳. "The Generation of Formosat-2 and Landsat-8 Time Series Images Using Spatiotemporal Fusion and Deep Learning Techniques." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/g5hexs.

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碩士<br>國立交通大學<br>土木工程系所<br>107<br>Time-series satellite images are important development for satellite imagery. The time-series satellite images are the integration of temporal sequences of images, which can analyze the spatial-temporal variations of any position on the earth's surface. Spatio-temporal fusion technique has the characteristics of generating high-spatial and high-temporal resolution image from different sensors. It can improve the ability to construct time-series images. Spatio-temporal image fusion includes five categories: unmixing-based, weight function-based, Bayesian-based,
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