2026-09-19T05:55:51Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1216342026-06-06T00:55:58Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Boris Katz.
Myanganbayar, Battushig.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-07-15T20:29:44Z
2019-07-15T20:29:44Z
2018
2018
https://hdl.handle.net/1721.1/121634
1098178842
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 77-79).
Hand pose recognition is a fundamental human ability and an important, yet elusive, goal for computer vision research. One of the major challenges in hand pose recognition is the sheer scale of the problem. The human hand is a notoriously agile object with 27 degrees of freedom. In a sense, it is an impossible task to collect a dataset with every major hand pose configuration. However, current state-of-the-art approaches rely too much on training data and generalize poorly to unseen hand poses. Furthermore, current benchmarking datasets are of poor quality and contain test sets that are highly correlated with the training set, which in turn encourages the development of data-reliant techniques for better accuracy only on paper. In this thesis, I introduce a better and more realistic benchmarking dataset, and propose a novel approach for hand pose detection that has the potential to generalize better to unseen hand poses.
by Battushig Myanganbayar.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
79 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A deeper look at hand pose estimation
Thesis
application/pdf
2019-07-15T20:29:42Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Hand pose recognition is a fundamental human ability and an important, yet elusive, goal for computer vision research. One of the major challenges in hand pose recognition is the sheer scale of the problem. The human hand is a notoriously agile object with 27 degrees of freedom. In a sense, it is an impossible task to collect a dataset with every major hand pose configuration. However, current state-of-the-art approaches rely too much on training data and generalize poorly to unseen hand poses. Furthermore, current benchmarking datasets are of poor quality and contain test sets that are highly correlated with the training set, which in turn encourages the development of data-reliant techniques for better accuracy only on paper. In this thesis, I introduce a better and more realistic benchmarking dataset, and propose a novel approach for hand pose detection that has the potential to generalize better to unseen hand poses.</Abstract>
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