A variational principle for model-based morphing

Advances in Neural Information Processing Systems

Volumn , Issue , 1997, Pages 267-273

  Saul, Lawrence K ^a   Jordan, Michael I ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EQUATIONS OF MOTION; VARIATIONAL TECHNIQUES;

ARC LENGTH; EULER-LAGRANGE EQUATIONS; GAUSSIANS; HIGH DIMENSIONS; MIXTURE MODEL; MULTIDIMENSIONAL DATA; OPTIMAL INTERPOLATION; VARIATIONAL PRINCIPLES;

INTERPOLATION;

EID: 0039084973     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. T. Poggio and F. Girosi. Networks for approximation and learning. Proc. of IEEE, vol 78:9 (1990).
2. C. Bregler and S. Omohundro. Nonlinear image interpolation using manifold learning. In G. Tesauro, D. Touretzky, and T. Leen (eds.). Advances in Neural Information Processing Systems 7, 973-980. MIT Press, Cambridge, MA (1995).
3. T. Ezzat. Example based analysis and synthesis for images of faces. MIT EECS M.S. thesis (1996).
4. D. Beymer, A. Shashua, and T. Poggio. Example based image analysis and synthesis. AI Memo 1161, MIT (1993).
5. H. Goldstein. Classical Mechanics. Addison-Wesley, London (1980).