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Transductive graph based cartoon synthesis.

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Title: Transductive graph based cartoon synthesis.
Authors: Yu, Jun yujun@ntu.edu.sg, Liu, Dongquan, Seah, Hock Soon
Source: Computer Animation & Virtual Worlds. May2010, Vol. 21 Issue 3/4, p277-288. 12p. 2 Color Photographs, 6 Diagrams, 1 Graph.
Subjects: Caricatures & cartoons, Computer-aided imagery software, Dynamic programming, Hausdorff measures, Cutout animation films
Abstract: To reduce tedious works in cartoon animation, some computer-assisted systems including automatic inbetweening and cartoon reusing systems have been proposed. In existing automatic inbetweening systems, accurate correspondence construction, which is a prerequisite for inbetweening, cannot be achieved. For cartoon reusing systems, the lack of efficient similarity estimation method and reusing mechanism makes it impractical for the users. The Transductive Graph based Cartoon Synthesis (TGCS) approach proposed in this paper aims at synthesizing smooth cartoons from the existing data. In this approach, the similarity between cartoon frames can be accurately evaluated by calculating the distance based on local shape context, which is rotation, and scaling invariant. According to the similarity, the label propagation based graph transduction method is adopted to generate cartoon clips, which is smoother than the clips generated by the shortest path method used in previous cartoon reusing approaches. Besides, the synthesized cartoon clips can be applied in accurate correspondence building, based on which the inbetweening method can be used to refine the results. Experimental results on our cartoon dataset suggest the effectiveness of the proposed approach for cartoon synthesis. Additional experiments on correspondence show our approach's performance on accurate correspondence building. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
Copyright of Computer Animation & Virtual Worlds is the property of Wiley-Blackwell 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.)
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  Data: Transductive graph based cartoon synthesis.
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  Data: <searchLink fieldCode="AR" term="%22Yu%2C+Jun%22">Yu, Jun</searchLink><i> yujun@ntu.edu.sg</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Dongquan%22">Liu, Dongquan</searchLink><br /><searchLink fieldCode="AR" term="%22Seah%2C+Hock+Soon%22">Seah, Hock Soon</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Computer+Animation+%26+Virtual+Worlds%22">Computer Animation & Virtual Worlds</searchLink>. May2010, Vol. 21 Issue 3/4, p277-288. 12p. 2 Color Photographs, 6 Diagrams, 1 Graph.
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  Data: <searchLink fieldCode="DE" term="%22Caricatures+%26+cartoons%22">Caricatures & cartoons</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-aided+imagery+software%22">Computer-aided imagery software</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+programming%22">Dynamic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Hausdorff+measures%22">Hausdorff measures</searchLink><br /><searchLink fieldCode="DE" term="%22Cutout+animation+films%22">Cutout animation films</searchLink>
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  Data: To reduce tedious works in cartoon animation, some computer-assisted systems including automatic inbetweening and cartoon reusing systems have been proposed. In existing automatic inbetweening systems, accurate correspondence construction, which is a prerequisite for inbetweening, cannot be achieved. For cartoon reusing systems, the lack of efficient similarity estimation method and reusing mechanism makes it impractical for the users. The Transductive Graph based Cartoon Synthesis (TGCS) approach proposed in this paper aims at synthesizing smooth cartoons from the existing data. In this approach, the similarity between cartoon frames can be accurately evaluated by calculating the distance based on local shape context, which is rotation, and scaling invariant. According to the similarity, the label propagation based graph transduction method is adopted to generate cartoon clips, which is smoother than the clips generated by the shortest path method used in previous cartoon reusing approaches. Besides, the synthesized cartoon clips can be applied in accurate correspondence building, based on which the inbetweening method can be used to refine the results. Experimental results on our cartoon dataset suggest the effectiveness of the proposed approach for cartoon synthesis. Additional experiments on correspondence show our approach's performance on accurate correspondence building. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of Computer Animation & Virtual Worlds is the property of Wiley-Blackwell 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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        Value: 10.1002/cav.355
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        Text: English
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        PageCount: 12
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      – SubjectFull: Caricatures & cartoons
        Type: general
      – SubjectFull: Computer-aided imagery software
        Type: general
      – SubjectFull: Dynamic programming
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      – SubjectFull: Hausdorff measures
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      – SubjectFull: Cutout animation films
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      – TitleFull: Transductive graph based cartoon synthesis.
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            NameFull: Liu, Dongquan
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              M: 05
              Text: May2010
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              Y: 2010
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