Audio-Adaptive Animation from Still Image

  • Konstantin Kryzhanovsky
  • Aleksey Vil’kin
  • Ilia Safonov
  • Zoya Pushchina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7870)

Abstract

In this paper we propose new approach of automatic generating real time content adaptive animation effects from the still images adapted for the low-powerful embedded HW platforms. Displayed animation behaves uniquely each time it’s played back, and does not repeat itself during playback duration, creating vivid and lively impression for the viewer. Adaptation of the effect parameters according to background audio greatly increases aesthetic impression of the viewer. Three animation effects such as Flashing Light, Soap Bubbles and Sunlight Spot are described in details. We propose several ways of controlling the effect parameters by music. User opinion survey demonstrates that majority of users are excited by such effects and wants to see them in their devices with multimedia capability.

Keywords

animation from photo audio-adaptive effect multimedia slideshow attention zones detection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chen, J., Xiao, J., Gao, Y.: iSlideshow: a Content-Aware Slideshow System. In: ACM Intelligent User Interface Conf., Hong Kong, pp. 293–296 (2010)Google Scholar
  2. 2.
    Sakaino, H.: The photodynamic tool: generation of animation from a single texture image. In: IEEE ICME, Amsterdam (2005)Google Scholar
  3. 3.
    Safonov, I., Bucha, V.: Animated thumbnail for still image. In: GRAPHICON 2010, St. Petersburg, pp. 79–86 (2010)Google Scholar
  4. 4.
    Chen, J.C., Chu, W.T., Kuo, J.H., Weng, C.Y., Wu, J.L.: Tiling slideshow. In: ACM Multimedia 2006, Santa Barbara, pp. 25–35 (2006)Google Scholar
  5. 5.
    Dunker, P., Popp, P., Cook, R.: Content-aware auto-soundtracks for personal photo music slideshows. In: IEEE ICME 2011, Barcelona, pp. 1–5 (2011)Google Scholar
  6. 6.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. of Conference Computer Vision and Pattern Recognition, Kauai, pp. 511–518 (2001)Google Scholar
  7. 7.
    Egorova, M.A., Murynin, A.B., Safonov, I.V.: An Improvement of face detection algorithm for color photos. Pattern Recognition and Image Analysis 19(4), 634–640 (2009)CrossRefGoogle Scholar
  8. 8.
    Vil’kin, A.M., Safonov, I.V., Egorova, M.A.: Bottom-up Document Segmentation Method Based on Textural Features. Pattern Recognition and Image Analysis 21(3), 565–568 (2011)CrossRefGoogle Scholar
  9. 9.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  10. 10.
    Cheng, M.M., Zhang, G.X., Mitra, N.J., Huang, X., Hu, S.M.: Global Contrast based Salient Region Detection. In: IEEE CVPR 2011, Colorado, pp. 409–416 (Springs 2011)Google Scholar
  11. 11.
    Goto, M.: Real-time music-scene-description system: predominant-F0 estimation for detecting melody and bass lines in real-world audio signals. Speech Communication 43(4), 311–329 (2004)CrossRefGoogle Scholar
  12. 12.
    Dixon, S.: Audio Beat Tracking Evaluation: BeatRoot. In: MIREX at 7th International ISMIR 2006 Conference, Victoria (2006)Google Scholar
  13. 13.
    Scheirer, E.D.: Tempo and beat analysis of acoustic musical signals. J. Acoust. Soc. Amer. 103(1), 588–601 (1998)CrossRefGoogle Scholar
  14. 14.
    McKinney, M.F., Moelants, D., Davies, M.E.P., Klapuri, A.: Evaluation of Audio Beat Tracking and Music Tempo Extraction Algorithms. Journal of New Music Research 36(1), 1–16 (2007)CrossRefGoogle Scholar
  15. 15.
    Vezhnevets, A., Vezhnevets, V.: Modest AdaBoost – teaching AdaBoost to generalize better. In: Proc. of Graphicon Conf., Moscow, pp. 322–325 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Konstantin Kryzhanovsky
    • 1
  • Aleksey Vil’kin
    • 1
  • Ilia Safonov
    • 1
  • Zoya Pushchina
    • 1
  1. 1.Samsung Moscow Research CenterMoscowRussia

Personalised recommendations