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Development of an efficient RGB-D annotation tool for video sequence
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Ren, Baozhang. Development of an efficient RGB-D annotation tool for video sequence. Retrieved from https://doi.org/doi:10.7282/t3-q1nb-4932

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TitleDevelopment of an efficient RGB-D annotation tool for video sequence
NameRen, Baozhang (author); Bekris, Kostas (chair); Yu, Jingjin (internal member); Boularias, Abdeslam (internal member); Rutgers University; School of Graduate Studies
Date Created2020
Other Date2020-01 (degree)
SubjectComputer Science, RGBD, Image processing -- Digital techniques
Extent1 online resource (v, 19 pages) : illustrations
DescriptionWith 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.
NoteM.S.
NoteIncludes bibliographical references
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/t3-q1nb-4932
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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