Thistimely and authoritative volume explores the bidirectional relationship betweenimages and locations. The text presents a comprehensive review of the state ofthe art in large-scale visual geo-localization, and discusses the emergingtrends in this area. Valuable insights are supplied by a pre-eminent selectionof experts in the field, into a varied range of real-world applications ofgeo-localization. Topics and features: discusses the latest methods to exploitinternet-scale image databases for devising geographically rich features andgeo-localizing query images at different scales;…mehr
Thistimely and authoritative volume explores the bidirectional relationship betweenimages and locations. The text presents a comprehensive review of the state ofthe art in large-scale visual geo-localization, and discusses the emergingtrends in this area. Valuable insights are supplied by a pre-eminent selectionof experts in the field, into a varied range of real-world applications ofgeo-localization. Topics and features: discusses the latest methods to exploitinternet-scale image databases for devising geographically rich features andgeo-localizing query images at different scales; investigates geo-localizationtechniques that are built upon high-level and semantic cues; describes methodsthat perform precise localization by geometrically aligning the query imageagainst a 3D model; reviews techniques that accomplish image understandingassisted by the geo-location, as well as several approaches for geo-localizationunder practical, real-world settings.
Produktdetails
Produktdetails
Advances in Computer Vision and Pattern Recognition
Dr. Amir R. Zamir is a postdoctoral researcher at the Computer Science Department of Stanford University, CA, USA. Dr. Asaad Hakeem is a Principal Research Scientist in the Machine Learning Division at Decisive Analytics Corporation, Arlington, VA, USA. Dr. Luc Van Gool is a Full Professor and Head of the Computer Vision Lab at ETH Zurich, Switzerland, and the VISICS Computer Vision at KU Leuven, Belgium. His other publications include the Springer title Detection and Identification of Rare Audio-visual Cues. Dr. Mubarak Shah is Agere Chair Professor and Director of the Center for Research in Computer Vision at the University of Central Florida, Orlando, FL, USA. He is the Series Editor of Springer's International Series in Video Computing, and he served as an Editor-in-Chief of the Springer journal Machine Vision and Applications from 2004 to 2015. Dr. Richard Szeliski is the Director and a founding member of the Computational Photography applied research group at Facebook, Seattle, WA, USA. He is also the author of the best-selling Springer textbook Computer Vision - Algorithms and Applications.
Inhaltsangabe
Introduction to Large Scale Visual Geo-Localization.- Part I: Data-Driven Geo-Localization.- Discovering Mid-Level Visual Connections in Space and Time.- Where the Photos Were Taken: Location Prediction by Learning from Flickr Photos.- Cross-View Image Geo-Localization.- Ultra-Wide Baseline Facade Matching for Geo-Localization.- Part II: Semantic Reasoning-Based Geo-Localization.- Semantically Guided Geo-Localization and Modeling in Urban Environments.- Recognizing Landmarks in Large-Scale Social Image Collections.- Part III: Geometric Matching-Based Geo-Localization.- Worldwide Pose Estimation Using 3D Point Clouds.- Exploiting Spatial and Co-Visibility Relations for Image-Based Localization.- 3D Point Cloud Reduction Using Mixed-Integer Quadratic Programming.- Image-Based Large-Scale Geo-Localization in Mountainous Regions.- Adaptive Rendering for Large-Scale Skyline Characterization and Matching.- User-Aided Geo-Localization of Untagged Desert Imagery.- Visual Geo-Localization of Non-Photographic Depictions via 2D-3D Alignment.- Part IV: Real-World Applications.- A Memory Efficient Discriminative Approach for Location-Aided Recognition.- A Real-World System for Image/Video Geo-Localization.- Photo Recall: Using the Internet to Label Your Photos.
Introduction to Large Scale Visual Geo-Localization.- Part I: Data-Driven Geo-Localization.- Discovering Mid-Level Visual Connections in Space and Time.- Where the Photos Were Taken: Location Prediction by Learning from Flickr Photos.- Cross-View Image Geo-Localization.- Ultra-Wide Baseline Facade Matching for Geo-Localization.- Part II: Semantic Reasoning-Based Geo-Localization.- Semantically Guided Geo-Localization and Modeling in Urban Environments.- Recognizing Landmarks in Large-Scale Social Image Collections.- Part III: Geometric Matching-Based Geo-Localization.- Worldwide Pose Estimation Using 3D Point Clouds.- Exploiting Spatial and Co-Visibility Relations for Image-Based Localization.- 3D Point Cloud Reduction Using Mixed-Integer Quadratic Programming.- Image-Based Large-Scale Geo-Localization in Mountainous Regions.- Adaptive Rendering for Large-Scale Skyline Characterization and Matching.- User-Aided Geo-Localization of Untagged Desert Imagery.- Visual Geo-Localization of Non-Photographic Depictions via 2D-3D Alignment.- Part IV: Real-World Applications.- A Memory Efficient Discriminative Approach for Location-Aided Recognition.- A Real-World System for Image/Video Geo-Localization.- Photo Recall: Using the Internet to Label Your Photos.
Introduction to Large Scale Visual Geo-Localization.- Part I: Data-Driven Geo-Localization.- Discovering Mid-Level Visual Connections in Space and Time.- Where the Photos Were Taken: Location Prediction by Learning from Flickr Photos.- Cross-View Image Geo-Localization.- Ultra-Wide Baseline Facade Matching for Geo-Localization.- Part II: Semantic Reasoning-Based Geo-Localization.- Semantically Guided Geo-Localization and Modeling in Urban Environments.- Recognizing Landmarks in Large-Scale Social Image Collections.- Part III: Geometric Matching-Based Geo-Localization.- Worldwide Pose Estimation Using 3D Point Clouds.- Exploiting Spatial and Co-Visibility Relations for Image-Based Localization.- 3D Point Cloud Reduction Using Mixed-Integer Quadratic Programming.- Image-Based Large-Scale Geo-Localization in Mountainous Regions.- Adaptive Rendering for Large-Scale Skyline Characterization and Matching.- User-Aided Geo-Localization of Untagged Desert Imagery.- Visual Geo-Localization of Non-Photographic Depictions via 2D-3D Alignment.- Part IV: Real-World Applications.- A Memory Efficient Discriminative Approach for Location-Aided Recognition.- A Real-World System for Image/Video Geo-Localization.- Photo Recall: Using the Internet to Label Your Photos.
Introduction to Large Scale Visual Geo-Localization.- Part I: Data-Driven Geo-Localization.- Discovering Mid-Level Visual Connections in Space and Time.- Where the Photos Were Taken: Location Prediction by Learning from Flickr Photos.- Cross-View Image Geo-Localization.- Ultra-Wide Baseline Facade Matching for Geo-Localization.- Part II: Semantic Reasoning-Based Geo-Localization.- Semantically Guided Geo-Localization and Modeling in Urban Environments.- Recognizing Landmarks in Large-Scale Social Image Collections.- Part III: Geometric Matching-Based Geo-Localization.- Worldwide Pose Estimation Using 3D Point Clouds.- Exploiting Spatial and Co-Visibility Relations for Image-Based Localization.- 3D Point Cloud Reduction Using Mixed-Integer Quadratic Programming.- Image-Based Large-Scale Geo-Localization in Mountainous Regions.- Adaptive Rendering for Large-Scale Skyline Characterization and Matching.- User-Aided Geo-Localization of Untagged Desert Imagery.- Visual Geo-Localization of Non-Photographic Depictions via 2D-3D Alignment.- Part IV: Real-World Applications.- A Memory Efficient Discriminative Approach for Location-Aided Recognition.- A Real-World System for Image/Video Geo-Localization.- Photo Recall: Using the Internet to Label Your Photos.
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Internetauftritt der buecher.de internetstores GmbH
Geschäftsführung: Monica Sawhney | Roland Kölbl | Günter Hilger
Sitz der Gesellschaft: Batheyer Straße 115 - 117, 58099 Hagen
Postanschrift: Bürgermeister-Wegele-Str. 12, 86167 Augsburg
Amtsgericht Hagen HRB 13257
Steuernummer: 321/5800/1497
