Image morphing based on mutual information and optimal mass transport

Time domain image interpolation, or image morphing, refers to a class of techniques for generating a series of smoothly changing intermediate images between two given related images. In this note, we present a novel approach based on the theoiy of optimal mass transport, using mutual information (MI) as the similarity measurement. The potential applications also include image registmtion, compression and coding.

[1]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[2]  J. Moser On the volume elements on a manifold , 1965 .

[3]  Sung Yong Shin,et al.  Image Morphing Using Deformation Techniques , 1996 .

[4]  David G. Stork,et al.  Pattern Classification , 1973 .

[5]  O. Faugeras,et al.  A variational approach to multi-modal image matching , 2001, Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision.

[6]  George Wolberg,et al.  Recent advances in image morphing , 1996, Proceedings of CG International '96.

[7]  Thaddeus Beier,et al.  Feature-based image metamorphosis , 1998 .

[8]  Lei Zhu,et al.  Image interpolation based on optimal mass preserving mappings , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[9]  Sung Yong Shin,et al.  Image Morphing Using Deformation Techniques , 1996, Comput. Animat. Virtual Worlds.

[10]  Steven Haker,et al.  Minimizing Flows for the Monge-Kantorovich Problem , 2003, SIAM J. Math. Anal..

[11]  W. Gangbo,et al.  The geometry of optimal transportation , 1996 .

[12]  George Wolberg,et al.  Image morphing: a survey , 1998, The Visual Computer.

[13]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .