Home
Resource
Visual localization, semantic video segmentation and labeling using satellite maps
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(9.09 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Senlet, Turgay. Visual localization, semantic video segmentation and labeling using satellite maps. Retrieved from https://doi.org/doi:10.7282/T3JS9S9Z

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

TitleVisual localization, semantic video segmentation and labeling using satellite maps
NameSenlet, Turgay (author); Elgammal, Ahmed (chair); Metaxas, Dimitris N (internal member); Bekris, Kostas (internal member); Ogale, Abhijit S (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2015
Other Date2015-05 (degree)
SubjectComputer Science, Image segmentation, Visual perception
Extent1 online resource (xxi, 163 p. : ill.)
DescriptionIn this dissertation, I propose vision-based geo-localization and segmentation methods that make use of semantic and appearance information from satellite images. First, I present a framework for vision-based localization of moving platforms by registering perspective camera images to satellite maps and by employing particle filter tracking techniques. I present different versions of the localization framework for stereo and monocular imagery. Second, I propose a novel computer vision method that uses semantic elements for efficient localization of a given aerial image in a large search area. In this method, I use buildings on the aerial image as semantic elements and make use of building neighborhood structures to obtain accurate localization results, efficiently. For this problem, I perform tests on a very large city building dataset with 300K buildings. Third, I propose a novel framework for semantic segmentation and labeling of videos that propagates semantic information from satellite maps on to globally localized video frames. This method generates accurate labeling of semantic elements without performing any prior learning on the video itself. Finally, in order to understand and extract semantic information from satellite images, I investigate algorithms for semantically labeling satellite images; mainly focusing on labeling buildings, roads, sidewalks, and crosswalks from satellite images. I propose novel techniques to estimate sidewalk paths occluded by trees on satellite images.
NotePh.D.
NoteIncludes bibliographical references
Noteby Turgay Senlet
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3JS9S9Z
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024