Large-Scale Visual Geo-Localization

1. Auflage 2016
ISBN: 978-3-319-25779-2
Verlag: Springer International Publishing

Buch, Englisch, 351 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 7342 g

Reihe: Advances in Computer Vision and Pattern Recognition

This
timely and authoritative volume explores the bidirectional relationship between
images and locations. The text presents a comprehensive review of the state of
the art in large-scale visual geo-localization, and discusses the emerging
trends in this area. Valuable insights are supplied by a pre-eminent selection
of experts in the field, into a varied range of real-world applications of
geo-localization. Topics and features: discusses the latest methods to exploit
internet-scale image databases for devising geographically rich features and
geo-localizing query images at different scales; investigates geo-localization
techniques that are built upon high-level and semantic cues; describes methods
that perform precise localization by geometrically aligning the query image
against a 3D model; reviews techniques that accomplish image understanding
assisted by the geo-location, as well as several approaches for geo-localization
under practical, real-world settings.

Zielgruppe

Research

Autoren/Hrsg.

Zamir, Amir R.

Hakeem, Asaad

Szeliski, Richard

Van Gool, Luc

Shah, Mubarak

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Geowissenschaften Geologie GIS, Geoinformatik
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision

Inhaltsverzeichnis

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.

Über Autor(innen)

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.