Détail de l'éditeur
University of London Press
localisé à :
Londres
|
Documents disponibles chez cet éditeur (3)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externes
Titre : Mapping crisis : participation, datafication and humanitarianism in the age of digital mapping Type de document : Monographie Auteurs : Doug Specht, Éditeur scientifique Editeur : Londres : University of London Press Année de publication : 2020 Importance : 259 p. ISBN/ISSN/EAN : 978-1-912250-38-7 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] analyse spatiale
[Termes IGN] cartographie collaborative
[Termes IGN] changement climatique
[Termes IGN] collecte de données
[Termes IGN] gestion de crise
[Termes IGN] participation du public
[Termes IGN] représentation cartographique
[Termes IGN] science citoyenne
[Termes IGN] visualisation de donnéesRésumé : (Editeur) The digital age has thrown questions of representation, participation and humanitarianism back to the fore, as machine learning, algorithms and big data centres take over the process of mapping the subjugated and subaltern. Since the rise of Google Earth in 2005, there has been an explosion in the use of mapping tools to quantify and assess the needs of those in crisis, including those affected by climate change and the wider neo-liberal agenda. Yet, while there has been a huge upsurge in the data produced around these issues, the representation of people remains questionable. Some have argued that representation has diminished in humanitarian crises as people are increasingly reduced to data points. In turn, this data has become ever more difficult to analyse without vast computing power, leading to a dependency on the old colonial powers to refine the data collected from people in crisis, before selling it back to them. This book brings together critical perspectives on the role that mapping people, knowledges and data now plays in humanitarian work, both in cartographic terms and through data visualisations, and questions whether, as we map crises, it is the map itself that is in crisis. Note de contenu : Introduction: mapping in times of crisis / Doug Specht
1. Mapping as tacit représentations of the colonial gaze / Tamara Bellone, Salvatore Engel- Di Mauro, Francesco Fiermonte, Emiliana Armano and Linda Quiquivix
2. The failures of participatory mapping: a mediational perspective / Gregory Asmolov
3. Knowledge and spatial production between old and new representations: a conceptual and operative Framework / Maria Rosaria Prisco
4. Data colonialism, surveillance capitalism and drones / Faine Greenwood
5. The role of data collection, mapping and analysis in the reproduction of refugeeness and migration discourses: reflections from the Refugee Spaces project / Giovanna Astolfo, Ricardo Marten Caceres, Garyfalia Palaiologou, Camillo Boano and Ed Manley
6. Dying in the technosphere: an intersectional analysis of European migration maps / Monika Halkort
7. Now the totality maps us: mapping climate migration and surveilling movable borders in digital cartographies / Bogna M. Konior
8. The rise of the citizen data scientist / Aleš Završnik and Pika Šarf
9. Modalities of united statelessness / Rupert AllanNuméro de notice : 26514 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.14296/920.9781912250387 En ligne : https://doi.org/10.14296/920.9781912250387 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97284
Titre : Robust hand pose recognition from stereoscopic capture Type de document : Thèse/HDR Auteurs : Rilwan Remilekun Basaru, Auteur Editeur : Londres : University of London Press Année de publication : 2018 Importance : 200 p. Format : 21 x 30 cm Note générale : bibliographie
A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy, Department of Computer Science, City, University of LondonLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] estimation de pose
[Termes IGN] image RVB
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] régression
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal siamoisRésumé : (auteur) Hand pose is emerging as an important interface for human-computer interaction. The problem of hand pose estimation from passive stereo inputs has received less attention in the literature compared to active depth sensors. This thesis seeks to address this gap by presenting a data-driven method to estimate a hand pose from a stereoscopic camera input, with experimental results comparable to more expensive active depth sensors. The frameworks presented in this thesis are based on a two camera stereo rig capture as it yields a simpler and cheaper set-up and calibration. Three frameworks are presented, describing the sequential steps taken to solve the problem of depth and pose estimation of hands.
The first is a data-driven method to estimate a high quality depth map of a hand from a stereoscopic camera input by introducing a novel regression framework. The method first computes disparity using a robust stereo matching technique. Then, it applies a machine learning technique based on Random Forest to learn the mapping between the estimated disparity and depth given ground truth data. We introduce Eigen Leaf Node Features (ELNFs) that perform feature selection at the leaf nodes in each tree to identify features that are most discriminative for depth regression. The system provides a robust method for generating a depth image with an inexpensive stereo camera.
The second framework improves on the task of hand depth estimation from stereo capture by introducing a novel superpixel-based regression framework that takes advantage of the smoothness of the depth surface of the hand. To this end, it introduces Conditional Regressive Random Forest (CRRF), a method that combines a Conditional Random Field (CRF) and a Regressive Random Forest (RRF) to model the mapping from a stereo RGB image pair to a depth image. The RRF provides a unary term that adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. While the RRF makes depth prediction for each super-pixel independently, the CRF unifies the prediction of depth by modeling pair-wise interactions between adjacent superpixels.
The final framework introduces a stochastic approach to propose potential depth solutions to the observed stereo capture and evaluate these proposals using two convolutional neural networks (CNNs). The first CNN, configured in a Siamese network architecture, evaluates how consistent the proposed depth solution is to the observed stereo capture. The second CNN estimates a hand pose given the proposed depth. Unlike sequential approaches that reconstruct pose from a known depth, this method jointly optimizes the hand pose and depth estimation through Markov-chain Monte Carlo (MCMC) sampling. This way, pose estimation can correct for errors in depth estimation, and vice versa.
Experimental results using an inexpensive stereo camera show that the proposed system measures pose more accurately than competing methods. More importantly, it presents the possibility of pose recovery from stereo capture that is on par with depth based pose recovery.Numéro de notice : 17505 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère En ligne : https://openaccess.city.ac.uk/19938/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90396 An introduction to the study of map projections / J.A. Steers (1956)
Titre : An introduction to the study of map projections Type de document : Guide/Manuel Auteurs : J.A. Steers, Auteur Mention d'édition : 10 Editeur : Londres : University of London Press Année de publication : 1956 Importance : 324 p. Format : 15 x 22 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Projections
[Termes IGN] projection
[Termes IGN] projection (équivalente) de Mollweide
[Termes IGN] projection azimutale
[Termes IGN] projection conique
[Termes IGN] projection cylindrique
[Termes IGN] projection de Hammer-Aitoff
[Termes IGN] projection orthographique
[Termes IGN] projection stéréographiqueIndex. décimale : 30.20 Projections - généralités Numéro de notice : 37574 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Manuel de cours Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=47452 Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 37574-01 30.20 Livre Centre de documentation Géodésie Disponible