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3D Rooftop Detection And Modeling Using Orthographic Aerial Images

Abstract Detection of extruded features like rooftops and trees in aerial images automatically is a very active area of research. Elevated features identified from aerial imagery have potential applications in urban planning, identifying cover in military training or flight training. Detection of such features using commonly available geospatial data like orthographic aerial imagery is very challenging because rooftop and tree textures are often camouflaged by similar looking features like roads, ground and grass. So, additonal data such as LIDAR, multispectral imagery and multiple viewpoints are exploited for more accurate detection. However, such data is often not available, or may be improperly registered or inacurate. In this thesis, we discuss ... (more)
Created Date 2013
Contributor Khanna, Kunal (Author) / Femiani, John (Advisor) / Wonka, Peter (Advisor) / Razdan, Anshuman (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Subject Computer science / building / detection / grabcut / reconstruction / rooftop / segmentation
Type Masters Thesis
Extent 59 pages
Language English
Reuse Permissions All Rights Reserved
Note M.S. Computer Science 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis