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Event-driven feature detection and tracking for visual SLAM / Ignacio Alzugaray (2022)  

Public Titre : Event-driven feature detection and tracking for visual SLAM Type de document : Thèse/HDR Auteurs : Ignacio Alzugaray, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2022 Note générale : bibliographie thesis submitted to attain the degree of Doctor of Sciences of ETH Zurich Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] caméra d'événement [Termes IGN] cartographie et localisation simultanées [Termes IGN] détection d'objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] image floue [Termes IGN] reconnaissance de formes [Termes IGN] séquence d'images [Termes IGN] vision par ordinateur Index. décimale : THESE Thèses et HDR Résumé : (auteur) Traditional frame-based cameras have become the de facto sensor of choice for a multitude of applications employing Computer Vision due to their compactness, low cost, ubiquity, and ability to provide information-rich exteroceptive measurements. Despite their dominance in the field, these sensors exhibit limitations in common, real-world scenarios where detrimental effects, such as motion blur during high-speed motion or over-/underexposure in scenes with poor illumination, are prevalent. Challenging the dominance of traditional cameras, the recent emergence of bioinspired event cameras has opened up exciting research possibilities for robust perception due to their high-speed sensing, High-Dynamic-Range capabilities, and low power consumption. Despite their promising characteristics, event cameras present numerous challenges due to their unique output: a sparse and asynchronous stream of events, only capturing incremental perceptual changes at individual pixels. This radically different sensing modality renders most of the traditional Computer Vision algorithms incompatible without substantial prior adaptation, as they are initially devised for processing sequences of images captured at fixed frame-rate. Consequently, the bulk of existing event-based algorithms in the literature have opted to discretize the event stream into batches and process them sequentially, effectively reverting to frame-like representations in an attempt to mimic the processing of image sequences from traditional sensors. Such event-batching algorithms have demonstrably outperformed other alternative frame-based algorithms in scenarios where the quality of conventional intensity images is severely compromised, unveiling the inherent potential of these new sensors and popularizing them. To date, however, many newly designed event-based algorithms still rely on a contrived discretization of the event stream for its processing, suggesting that the full potential of event cameras is yet to be harnessed by processing their output more naturally. This dissertation departs from the mere adaptation of traditional frame-based approaches and advocates instead for the development of new algorithms integrally designed for event cameras to fully exploit their advantageous characteristics. In particular, the focus of this thesis lies on describing a series of novel strategies and algorithms that operate in a purely event-driven fashion, \ie processing each event as soon as it gets generated without any intermediate buffering of events into arbitrary batches and thus avoiding any additional latency in their processing. Such event-driven processes present additional challenges compared to their simpler event-batching counterparts, which, in turn, can largely be attributed to the requirement to produce reliable results at event-rate, entailing significant practical implications for their deployment in real-world applications. The body of this thesis addresses the design of event-driven algorithms for efficient and asynchronous feature detection and tracking with event cameras, covering alongside crucial elements on pattern recognition and data association for this emerging sensing modality. In particular, a significant portion of this thesis is devoted to the study of visual corners for event cameras, leading to the design of innovative event-driven approaches for their detection and tracking as corner-events. Moreover, the presented research also investigates the use of generic patch-based features and their event-driven tracking for the efficient retrieval of high-quality feature tracks. All the developed algorithms in this thesis serve as crucial stepping stones towards a completely event-driven, feature-based Simultaneous Localization And Mapping (SLAM) pipeline. This dissertation extends upon established concepts from state-of-the-art, event-driven methods and further explores the limits of the event-driven paradigm in realistic monocular setups. While the presented approaches solely rely on event-data, the gained insights are seminal to future investigations targeting the combination of event-based vision with other, complementary sensing modalities. The research conducted here paves the way towards a new family of event-driven algorithms that operate efficiently, robustly, and in a scalable manner, envisioning a potential paradigm shift in event-based Computer Vision. Note de contenu : 1- Introduction 2- Contribution 3- Conclusion and outlook Numéro de notice : 28699 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Sciences : ETH Zurich : 2022 DOI : sans En ligne : https://www.research-collection.ethz.ch/handle/20.500.11850/541700 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100470

Global glacier mass change by spatiotemporal analysis of digital elevation models / Romain Hugonnet (2022)  

Public Titre : Global glacier mass change by spatiotemporal analysis of digital elevation models Titre original : Changement de masse des glaciers à l’échelle mondiale par analyse spatiotemporelle de modèles numériques de terrain Type de document : Thèse/HDR Auteurs : Romain Hugonnet, Auteur ; Etienne Berthier, Directeur de thèse ; Daniel Farinotti, Directeur de thèse Editeur : Toulouse : Université de Toulouse 3 Paul Sabatier Année de publication : 2022 Autre Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Importance : 244 p. Format : 21 x 30 cm Note générale : bibliographie Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, spécialité Océan, Atmosphère, Climat Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bilan de masse [Termes IGN] changement climatique [Termes IGN] cryosphère [Termes IGN] données spatiotemporelles [Termes IGN] fonte des glaces [Termes IGN] glacier [Termes IGN] image optique [Termes IGN] MNS ASTER [Termes IGN] modèle numérique de surface [Termes IGN] modèle stéréoscopique [Termes IGN] niveau de la mer Index. décimale : THESE Thèses et HDR Résumé : (auteur) The world's glaciers are shrinking rapidly, with impacts ranging from global sea-level rise and changes in freshwater availability to the alteration of cryospheric hazards. Despite significant advances during the satellite era, the monitoring of the mass changes of glaciers is still hampered by a fragmented coverage of remote sensing estimations and a poor constraint of the errors in related assessments. In this thesis, we present a globally complete and resolved estimate of glacier mass changes by spatiotemporal analysis of digital elevation models. We first develop methods based on spatiotemporal statistics to assess the accuracy and precision of digital elevation models, and to estimate time series of glacier surface elevation. In particular, we introduce a non-stationary spatial framework to estimate and propagate multi-scale spatial correlations in uncertainties of geospatial estimates. We then massively generate digital elevation models from two decades of stereo optical archives covering glaciers worldwide. From those, we estimate time series of surface elevation for all of Earth's glaciers at a resolution of 100 m during 2000-2019. Integrating these time series into volume and mass changes, we identify a significant acceleration of global glacier mass loss, as well as regionally contrasted responses that mirror decadal changes in climatic conditions. Using a large amount of independent, high-precision data, we demonstrate the validity of our analysis to yield reliable and consistent uncertainties at different scales of the spatiotemporal structure of our estimates. We expect our methods to foster robust spatiotemporal analyses, in to identify sources of biases and uncertainties in geospatial assessments. Furthermore, we anticipate our estimates to advance the understanding of the drivers that govern glacier change, and to extend our capabilities of predicting these changes at all scales. Such predictions are critically needed to design adaptive policies on the mitigation of cryospheric impacts in the context of climate change. Note de contenu : General introduction 1- Monitoring Earth’s glaciers: an observational challenge rooted in space and time 2- Analysis of accuracy and precision of digital elevation models 3- Spatiotemporal estimation of glacier surface elevation Conclusions and outlook Numéro de notice : 24035 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Océan, Atmosphère, Climat : Toulouse 3 : 2022 Organisme de stage : LEGOS DOI : sans En ligne : https://tel.hal.science/tel-03813744 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101852

Mapping power / Ben Ori Gitai (2022)  

Public Titre : Mapping power : Landscape transformation in the Jordan Valley Type de document : Thèse/HDR Auteurs : Ben Ori Gitai, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2022 Importance : 348 p. Format : 21 x 30 cm Note générale : Bibliographie A thesis submitted to attain the degree of Doctor of Sciences of ETH Zurich Langues : Français (fre) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] analyse diachronique [Termes IGN] bornage [Termes IGN] carte hydrographique [Termes IGN] cartographie ancienne [Termes IGN] document cartographique [Termes IGN] frontière [Termes IGN] géopolitique [Termes IGN] histoire [Termes IGN] irrigation [Termes IGN] Israël [Termes IGN] Jordanie [Termes IGN] lever topographique [Termes IGN] paysage [Termes IGN] territoire [Termes IGN] transformation Résumé : (auteur) The interaction of three variables—territory, cartography, and terrain—can account for landscape transformation processes at Naharayim/el Baqura, in the Jordan Valley, over the last century and a half. This interface is examined across three historical periods marking the passage from nomadism to sedentism: The Ottoman Period (1858–1917), the British Mandate (1918–1948), and the statehood period (1948–1994). Adopting a hybridized and interdisciplinary approach at the juncture of history, landscape architecture, and geopolitics, this work performs an in-depth analysis of the three variables in each period and examines their relationship to power. The analysis emphasizes territorial concepts and border-making, mapping practices, land survey techniques, infrastructure, agricultural development, and water regimes. It will be shown that mapping has been a medium of politics, with landscapes often being subjugated to political or territorial ambitions. Data is gathered from primary sources, including some previously unknown from the periods in question, fieldwork, interviews, a point cloud data set from both banks of the Jordan River, as well as contemporary social scientific scholarship. It is argued that landscape is neither a natural feature nor a man-made system of engineered spaces, but rather the outcome of a dynamic interaction between natural landscape, human imagination, and various iterations of power, be they natural, theological, technological, political, or military. It is this dynamic interaction across space and time that this work attempts to map. Note de contenu : 1. Domesticated Landscape: Ottoman Imperial Period (1858–1917) 2. Captive Landscape: British Mandate Period (1918–1948) 3. Buried Landscape: Israel and Jordan National Period (1948–1994) 4. Conclusion Numéro de notice : 24041 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère Note de thèse : PhD Thesis : Sciences : ETH Zurich : 2022 DOI : sans En ligne : https://www.research-collection.ethz.ch/handle/20.500.11850/549646 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101881

Scene understanding and gesture recognition for human-machine interaction / Naina Dhingra (2022)  

Public Titre : Scene understanding and gesture recognition for human-machine interaction Type de document : Thèse/HDR Auteurs : Naina Dhingra, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2022 Note générale : Bibliographie A dissertation submitted to attain the degree of Doctor of Sciences of ETH Zurich Langues : Français (fre) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification orientée objet [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] compréhension de l'image [Termes IGN] image RVB [Termes IGN] interaction homme-machine [Termes IGN] oculométrie [Termes IGN] reconnaissance automatique [Termes IGN] reconnaissance de formes [Termes IGN] reconnaissance de gestes [Termes IGN] réseau neuronal récurrent [Termes IGN] scène [Termes IGN] vision par ordinateur Résumé : (auteur) Scene understanding and gesture recognition are useful for a myriad of applications such as human-robotic interaction, assisting blind and visually impaired people, advanced driver assistance systems, and autonomous driving. To work autonomously in real-world environments, automatic systems need to deliver non-verbal information to enhance the verbal communication in particular for blind people. We are exploring the holistic approach for providing the scene as well as gesture related information. We propose that incorporating attention mechanisms in neural networks which behave similarly to attention in the human brain, and conducting an integrated study using neural networks in real-time can yield significant improvements in the scene and gesture understanding, thereby enhancing the user experience. In this thesis, we investigate the understanding of visual scenes and gestures. We explore these two areas, in particular, by proposing novel architectures, training methods, user studies, and thorough evaluations. We show that, for deep learning approaches, attention or self attention mechanisms improve and push the boundaries of network performance for different tasks in consideration. We suggest that the various kinds of gestures can complement and supplement each other’s information to better understand non-verbal conversation; hence integrated gestures comprehension is useful. First, we focus on visual scene understanding using scene graph generation. We propose, BGT-Net, a new network that uses an object detection model with 1) bidirectional gated recurrent units for object-object communication and 2) transformer encoders including self attention to classify the objects and their relationships. We address the problem of bias caused by the long tailed distribution in the dataset. This enables the network to perform even for the unseen objects or relationships in the dataset. Second, we propose to learn hand gesture recognition from RGB and RGB-D videos using attention learning. We present a novel architecture based on residual connections and an attention mechanism. Our approach successfully detects hand gestures when evaluated on three open-source datasets. Third, we explore pointing gesture recognition and localization using open-source software, i.e. OpenPtrack which uses a deep learning based iii network to track multi-persons in the scene. We use a Kinect sensor as an input device and conduct a user study with 26 users to evaluate the system using two setup types. Fourth, we propose a technique to perform eye gaze tracking using OpenFace which is based on a deep learning model and RGB webcam. We use support vector machine regression to estimate the position of eye gaze on the screen. In a study, we evaluate the system with 28 users and show that this system can perform similarly to commercially expensive eye trackers. Finally, we focus on 3D head pose estimation using two models: 1)headPosr includes residual connections for the base network followed by a transformer encoder. It outperforms existing models but has a drawback of being computationally expensive; 2) lwPosr uses depthwise separable convolutions and transformer encoders. It is a two stream network in fine-grained fashion to estimate the three angles of the head pose. We demonstrate that this method is able to predict head poses better than state-of-the-art lightweight networks. Note de contenu : 1- Introduction 2- Background 3- State of the art 4- Scene graph generation 5- 3D hand gesture recognition 6- Pointing gesture recognition 7- Eye-gaze tracking 8- Head pose estimation 9- Lightweight head pose estimation 10- Summary Numéro de notice : 24039 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD Thesis : Sciences : ETH Zurich :2022 DOI : sans En ligne : https://www.research-collection.ethz.ch/handle/20.500.11850/559347 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101876

Benefiting from local rigidity in 3D point cloud processing / Zan Gojcic (2021)  

Public Titre : Benefiting from local rigidity in 3D point cloud processing Type de document : Thèse/HDR Auteurs : Zan Gojcic, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2021 Importance : 141 p. Format : 21 x 30 cm Note générale : bibliographie A thesis submitted to attain the degree of Doctor of Sciences of ETH Zurich Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] capteur actif [Termes IGN] champ vectoriel [Termes IGN] déformation d'image [Termes IGN] données lidar [Termes IGN] effondrement de terrain [Termes IGN] enregistrement de données [Termes IGN] filtrage du bruit [Termes IGN] flux [Termes IGN] image 3D [Termes IGN] navigation autonome [Termes IGN] orientation du capteur [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] téléphone intelligent [Termes IGN] traitement de semis de points [Termes IGN] voxel Index. décimale : THESE Thèses et HDR Résumé : (auteur) Incorporating 3D understanding and spatial reasoning into (intelligent) algorithms is crucial for solving several tasks in fields such as engineering geodesy, risk assessment, and autonomous driving. Humans are capable of reasoning about 3D spatial relations even from a single 2D image. However, making the priors that we rely on explicit and integrating them into computer programs is very challenging. Operating directly on 3D input data, such as 3D point clouds, alleviates the need to lift 2D data into a 3D representation within the task-specific algorithm and hence reduces the complexity of the problem. The 3D point clouds are not only a better-suited input data representation, but they are also becoming increasingly easier to acquire. Indeed, nowadays, LiDAR sensors are even integrated into consumer devices such as mobile phones. However, these sensors often have a limited field of view, and hence multiple acquisitions are required to cover the whole area of interest. Between these acquisitions, the sensor has to be moved and pointed in a different direction. Moreover, the world that surrounds us is also dynamic and might change as well. Reasoning about the motion of both the sensor and the environment, based on point clouds acquired in two-time steps, is therfore an integral part of point cloud processing. This thesis focuses on incorporating rigidity priors into novel deep learning based approaches for dynamic 3D perception from point cloud data. Specifically, the tasks of point cloud registration, deformation analysis, and scene flow estimation are studied. At first, these tasks are incorporated into a common framework where the main difference is in the level of rigidity assumptions that are imposed on the motion of the scene or the acquisition sensor. Then, the tasks specific priors are proposed and incorporated into novel deep learning architectures. While the global rigidity can be assumed in point cloud registration, the motion patterns in deformation analysis and scene flow estimation are more complex. Therefore, the global rigidity prior has to be relaxed to local or instancelevel rigidity, respectively. Rigidity priors not only add structure to the aforementioned tasks, which prevents physically implausible estimates and improves the generalization of the algorithms, but in some cases also reduce the supervision requirements. The proposed approaches were quantitatively and qualitatively evaluated on several datasets, and they yield favorable performance compared to the state-of-the-art. Numéro de notice : 28660 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD : Sciences : ETH Zurich : 2021 DOI : sans En ligne : https://www.research-collection.ethz.ch/handle/20.500.11850/523368 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99817

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From point clouds to high-fidelity models - advanced methods for image-based 3D reconstruction / Audrey Richard (2021)  

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Collaborative visual-inertial state and scene estimation / Marco Karrer (2020)  

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Enhancing knowledge, skills, and spatial reasoning through location-based mobile learning / Christian Sailer (2020)  

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Modelling historical accessibility and its effects in space / Raphaël Fuhrer (2020)  

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Point cloud registration and mitigation of refraction effects for geomonitoring using long-range terrestrial laser scanning / Ephraim Friedli (2020)  

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Realistic modeling of power transmission lines with geographic information systems / Joram Schito (2020)  

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Convolutional neural network for traffic signal inference based on GPS traces / Yann Méneroux (2018) 

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Spatial big data and machine learning in GIScience, Workshop at GIScience 2018, Melbourne, Australia, 28 August 2018 / Martin Raubal (2018)    

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Monitoring rock glaciers by combining photogrammetric and GNSS-based methods / Fabian Neyer (2017)  

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