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An Experimental Facial Synthesis System Using Graph Cut and Gradient Domain Fusion

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Book cover Technologies for E-Learning and Digital Entertainment (Edutainment 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3942))

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Abstract

Based on the newly appeared image editing and image pro- cessing techniques, a novel interactive, computer-assisted system is proposed for facial synthesis. This paper presents the architecture of this facial synthesis system and gives a detailed description of the four key modules. The techniques used in these modules are also particularized. First, graph cut algorithm is used to automatically select region. Then gradient domain fusion is used to get better result. Finally, k-means method is used to improve efficiency. The experimental results show that our experimental facial synthesis system can produce visually good synthesized face images.

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References

  1. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)

    Article  Google Scholar 

  2. Ṕerez, P., Gangnet, M., Blake, A.: Poisson image editing. ACM Transactions on Graphics 22(3), 313–318 (2003)

    Google Scholar 

  3. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Press, Chichester (2000)

    Google Scholar 

  4. Agarwala, A., Dontcheva, M., Agrawala, M., Drucker, S., Colburn, A., Curless, B., Salesin, D., Cohen, M.: Interactive digital photomontage. In: Proceedings of ACM SIGGRAPH (2004)

    Google Scholar 

  5. Fattal, R., Lischinski, D., Werman, M.: Gradient domain high dynamic range compression. ACM Transactions on Graphics 21(3), 249–256 (2002)

    Article  Google Scholar 

  6. Jing, F.W., Liu, Y.W., Wang, S.S.: Application of Modified K-means clustering algorithm to Lithofacies Identification. Control & Automation 7, 41–42 (2004)

    Google Scholar 

  7. Li, Y., Sun, J., Tang, C.K., Shum, H.Y.: Lazy Snapping. SIGGRAPH (ACM Transaction on Graphics 23(3) (2004)

    Google Scholar 

  8. Boykov, Y., Kolmogorov, V.: An experimental comparison of mincut/max-flow algorithms for energy minimization in vision. In: Energy Minimization Methods in Computer Vision and Pattern Recognition (2001)

    Google Scholar 

  9. Kwatra, V., Schödl, A., Essa, I., Turk, G., Bobick, A.: Graphcut textures: Image and video synthesis using graph cuts. In: Proceedings of ACM SIGGRAPH (2003)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Jiang, X., Dai, F., Jiang, H. (2006). An Experimental Facial Synthesis System Using Graph Cut and Gradient Domain Fusion. In: Pan, Z., Aylett, R., Diener, H., Jin, X., Göbel, S., Li, L. (eds) Technologies for E-Learning and Digital Entertainment. Edutainment 2006. Lecture Notes in Computer Science, vol 3942. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11736639_127

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  • DOI: https://doi.org/10.1007/11736639_127

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33423-1

  • Online ISBN: 978-3-540-33424-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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