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Large-Scale Visual Geo-Localization

Overview of attention for book
Cover of 'Large-Scale Visual Geo-Localization'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Large-Scale Visual Geo-localization
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    Chapter 2 Discovering Mid-level Visual Connections in Space and Time
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    Chapter 3 Where the Photos Were Taken: Location Prediction by Learning from Flickr Photos
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    Chapter 4 Cross-View Image Geo-localization
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    Chapter 5 Ultrawide Baseline Facade Matching for Geo-localization
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    Chapter 6 Semantically Guided Geo-location and Modeling in Urban Environments
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    Chapter 7 Recognizing Landmarks in Large-Scale Social Image Collections
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    Chapter 8 Worldwide Pose Estimation Using 3D Point Clouds
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    Chapter 9 Exploiting Spatial and Co-visibility Relations for Image-Based Localization
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    Chapter 10 3D Point Cloud Reduction Using Mixed-Integer Quadratic Programming
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    Chapter 11 Image-Based Large-Scale Geo-localization in Mountainous Regions
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    Chapter 12 Adaptive Rendering for Large-Scale Skyline Characterization and Matching
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    Chapter 13 User-Aided Geo-location of Untagged Desert Imagery
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    Chapter 14 Visual Geo-localization of Non-photographic Depictions via 2Dā€“3D Alignment
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    Chapter 15 A Memory Efficient Discriminative Approach for Location-Aided Recognition
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    Chapter 16 A Real-World System for Image/Video Geo-localization
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    Chapter 17 Photo Recall: Using the Internet to Label Your Photos
Overall attention for this book and its chapters
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Citations

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Title
Large-Scale Visual Geo-Localization
Published by
Springer International Publishing, January 2016
DOI 10.1007/978-3-319-25781-5
ISBNs
978-3-31-925779-2, 978-3-31-925781-5
Editors

Amir R. Zamir, Asaad Hakeem, Luc Van Gool, Mubarak Shah, Richard Szeliski

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 40%
Student > Master 5 20%
Student > Bachelor 3 12%
Other 1 4%
Lecturer 1 4%
Other 3 12%
Unknown 2 8%
Readers by discipline Count As %
Computer Science 9 36%
Engineering 7 28%
Neuroscience 2 8%
Nursing and Health Professions 1 4%
Business, Management and Accounting 1 4%
Other 1 4%
Unknown 4 16%