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Reconstruction of 3D human body pose from stereo image sequences based on top-down learning

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Title: Reconstruction of 3D human body pose from stereo image sequences based on top-down learning
Authors: Yang, Hee-Deok1 hdyang@image.korea.ac.kr, Lee, Seong-Whan swlee@image.korea.ac.kr
Source: Pattern Recognition. Nov2007, Vol. 40 Issue 11, p3120-3131. 12p.
Subjects: Mind & body, Human anatomy, Human physiology, Human body
Abstract: Abstract: This paper presents a novel method for reconstructing a 3D human body pose from stereo image sequences based on a top-down learning method. However, it is inefficient to build a statistical model using all training data. Therefore, the training data is hierarchically divided into several clusters to reduce the complexity of the learning problem. In the learning stage, the human body model database is hierarchically constructed by classifying the training data into several sub-clusters with silhouette images. The data of each cluster in the bottom level is represented by a linear combination of examples. In the reconstruction stage, the proposed method hierarchically searches a cluster for the best matching silhouette image using a silhouette history image (SHI). Then, the 3D human body pose is reconstructed from a depth image using a linear combination of examples method. By using depth information to reconstruct 3D human body pose, the similar poses in silhouette images are estimated as different 3D human body poses. The experimental results demonstrate that the proposed method is efficient and effective for reconstructing 3D human body poses. [Copyright &y& Elsevier]
Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Reconstruction of 3D human body pose from stereo image sequences based on top-down learning
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  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Hee-Deok%22">Yang, Hee-Deok</searchLink><relatesTo>1</relatesTo><i> hdyang@image.korea.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Lee%2C+Seong-Whan%22">Lee, Seong-Whan</searchLink><i> swlee@image.korea.ac.kr</i>
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  Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition%22">Pattern Recognition</searchLink>. Nov2007, Vol. 40 Issue 11, p3120-3131. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Mind+%26+body%22">Mind & body</searchLink><br /><searchLink fieldCode="DE" term="%22Human+anatomy%22">Human anatomy</searchLink><br /><searchLink fieldCode="DE" term="%22Human+physiology%22">Human physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Human+body%22">Human body</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Abstract: This paper presents a novel method for reconstructing a 3D human body pose from stereo image sequences based on a top-down learning method. However, it is inefficient to build a statistical model using all training data. Therefore, the training data is hierarchically divided into several clusters to reduce the complexity of the learning problem. In the learning stage, the human body model database is hierarchically constructed by classifying the training data into several sub-clusters with silhouette images. The data of each cluster in the bottom level is represented by a linear combination of examples. In the reconstruction stage, the proposed method hierarchically searches a cluster for the best matching silhouette image using a silhouette history image (SHI). Then, the 3D human body pose is reconstructed from a depth image using a linear combination of examples method. By using depth information to reconstruct 3D human body pose, the similar poses in silhouette images are estimated as different 3D human body poses. The experimental results demonstrate that the proposed method is efficient and effective for reconstructing 3D human body poses. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.patcog.2007.01.033
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 12
        StartPage: 3120
    Subjects:
      – SubjectFull: Mind & body
        Type: general
      – SubjectFull: Human anatomy
        Type: general
      – SubjectFull: Human physiology
        Type: general
      – SubjectFull: Human body
        Type: general
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      – TitleFull: Reconstruction of 3D human body pose from stereo image sequences based on top-down learning
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            NameFull: Yang, Hee-Deok
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            NameFull: Lee, Seong-Whan
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              M: 11
              Text: Nov2007
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              Y: 2007
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              Value: 11
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            – TitleFull: Pattern Recognition
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