LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
With the prevalence of neural networks and deep learning models, more data is required to expand the domain as well as to improve the accuracy of those models. There are numerous annotation tools and software of RGB data aiming to make the labeling process less gradual and more efficient while maintaining the same accuracy as traditional methods. However, fewer such efforts have been made in the RGB-D domain. This paper provides a novel RGB-D annotation tool that is designed to efficiently generate object poses in images or video sequences. The tool is equipped with functions, such as removing background points, interactive marker, to aid annotation, as well as ICP to lower the number of frames that need to be labeled in a video sequence.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = local)
Topic
RGBD
Subject (authority = LCSH)
Topic
Image processing -- Digital techniques
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10433
PhysicalDescription
Form (authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (v, 19 pages) : illustrations
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
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