Zamir / Hakeem / Van Gool Large-Scale Visual Geo-Localization

1. Auflage 2016
ISBN: 978-3-319-25781-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 351 Seiten, eBook

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

Van Gool, Luc

Shah, Mubarak

Szeliski, Richard

Inhaltsverzeichnis

Introduction to
Large Scale Visual Geo-Localization
Amir R. Zamir, Asaad Hakeem, Luc Van
Gool, Mubarak Shah, and Richard Szeliski

Part I: Data-Driven Geo-Localization

Discovering
Mid-Level Visual Connections in Space and Time
Yong Jae Lee, Alexei A. Efros, and
Martial Hebert

Where the Photos
Were Taken: Location Prediction by Learning from Flickr Photos
Li-Jia Li, Rahul Kumar Jha, Bart Thomee,
David Ayman Shamma, Liangliang Cao, and Yang Wang

Cross-View Image
Geo-Localization
Tsung-Yi Lin, Serge Belongie, and James
Hays

Ultra-Wide
Baseline Facade Matching for Geo-Localization
Mayank Bansal, Kostas Daniilidis, and
Harpreet Sawhney

Part II: Semantic Reasoning-Based Geo-Localization

Semantically Guided
Geo-Localization and Modeling in Urban Environments
Gautam Singh and Jana Košecká

Recognizing
Landmarks in Large-Scale Social Image Collections
David J. Crandall, Yunpeng Li, Stefan
Lee, and Daniel P. Huttenlocher

Part III: Geometric Matching-Based Geo-Localization

Worldwide Pose
Estimation Using 3D Point Clouds
Yunpeng Li, Noah Snavely, Dan
Huttenlocher, and Pascal Fua

Exploiting
Spatial and Co-Visibility Relations for Image-Based Localization
Torsten Sattler, Bastian Leibe, and Leif
Kobbelt

<3D Point Cloud
Reduction Using Mixed-Integer Quadratic Programming
Hyun Soo Park, Yu Wang, Eriko Nurvitadhi,
James C. Hoe, Yaser Sheikh, and Mei Chen

Image-Based
Large-Scale Geo-Localization in Mountainous Regions
Olivier Saurer, Georges Baatz, Kevin
Köser, L’ubor Ladický, and Marc Pollefeys

Adaptive
Rendering for Large-Scale Skyline Characterization and Matching
Jiejie Zhu, Mayank Bansal, Nick Vander
Valk, and Hui Cheng

User-Aided Geo-Localization
of Untagged Desert Imagery

Visual Geo-Localization
of Non-Photographic Depictions via 2D-3D Alignment
Mathieu Aubry, Bryan Russell, and Josef
Sivic

Part IV: Real-World Applications

A Memory
Efficient Discriminative Approach for Location-Aided Recognition
Sudipta N. Sinha, Varsha Hedau, C. Lawrence
Zitnick, and Richard Szeliski

A Real-World
System for Image/Video Geo-Localization
Himaanshu Gupta, Yi Chen, Minwoo Park,
Kiran Gunda, Gang Qian, Dave Conger, and Khurram Shafique

Photo Recall:
Using the Internet to Label Your Photos
Neeraj Kumar and Steven Seitz

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.