Automatic Generation of Subject-Based Image Transitions

  • Edoardo Ardizzone
  • Roberto Gallea
  • Marco La Cascia
  • Marco Morana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)


This paper presents a novel approach for the automatic generation of image slideshows. Counter to standard cross-fading, the idea is to operate the image transitions keeping the subject focused in the intermediate frames by automatically identifying him/her and preserving face and facial features alignment. This is done by using a novel Active Shape Model and time-series Image Registration. The final result is an aesthetically appealing slideshow which emphasizes the subject. The results have been evaluated with a users’ response survey. The outcomes show that the proposed slideshow concept is widely preferred by final users w.r.t. standard image transitions.


Face processing image morphing image registration 


  1. 1.
    Lo Presti, L., Morana, M., La Cascia, M.: A data association algorithm for people re-identification in photo sequences. In: 2010 IEEE International Symposium on Multimedia (ISM), pp. 318–323 (2010)Google Scholar
  2. 2.
    Min, F., Lu, T., Zhang, Y.: Automatic face replacement in photographs based on active shape models. In: Asia-Pacific Conference on Computational Intelligence and Industrial Applications, PACIIA 2009, vol. 1, pp. 170–173 (2009)Google Scholar
  3. 3.
    Terada, T., Fukui, T., Igarashi, T., Nakao, K., Kashimoto, A.: Yen-Wei Chen. Automatic facial image manipulation system and facial texture analysis. In: Fifth International Conference on Natural Computation, ICNC 2009, vol. 6, pp. 8–12 (2009)Google Scholar
  4. 4.
    Fantamorph. Abrosoft (2010),
  5. 5.
    Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002)CrossRefGoogle Scholar
  6. 6.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  7. 7.
    Yuille, A.L., Cohen, D.S., Hallinan, P.W.: Feature extraction from faces using deformable templates. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings CVPR 1989, pp. 104–109 (June 1989)Google Scholar
  8. 8.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models—their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)CrossRefGoogle Scholar
  9. 9.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision V1(4), 321–331 (1988)CrossRefzbMATHGoogle Scholar
  10. 10.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, p. 484. Springer, Heidelberg (1998)Google Scholar
  11. 11.
    Gallagher, A., Chen, T.: Clothing cosegmentation for recognizing people. In: Proc. CVPR (2008)Google Scholar
  12. 12.
    Gallea, R., Ardizzone, E., Gambino, O., Pirrone, R.: Multi-modal image registration using fuzzy kernel regression. In: ICIP 2009, pp. 193–196 (2009)Google Scholar
  13. 13.
    Bookstein, F.L.: Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 567–585 (1989)CrossRefzbMATHGoogle Scholar
  14. 14.
    Penner, R.: Programming Macromedia Flash MX. McGraw-Hill, New York (2002)Google Scholar
  15. 15.
    Rubinstein, M., Gutierrez, D., Sorkine, O., Shamir, A.: A comparative study of image retargeting. ACM Transactions on Graphics (SIGGRAPH) 29(5) (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Edoardo Ardizzone
    • 1
  • Roberto Gallea
    • 1
  • Marco La Cascia
    • 1
  • Marco Morana
    • 1
  1. 1.Università degli Studi di PalermoItaly

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