Image reconstruction by linear programming

Advances in Neural Information Processing Systems

Volumn , Issue , 2004, Pages

  Tsuda, Koji ^a,b   Rätsch, Gunnar ^a,c  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^b NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

^c FRAUNHOFER FIRST   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE RECONSTRUCTION; LINEAR PROGRAMMING;

BOUNDARY POINTS; INTERIOR POINT; LINEAR PROGRAMS; NOISY IMAGE; NOISY PIXELS; PRIOR KNOWLEDGE;

PIXELS;

EID: 33947326532     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. A. Ben Hamza and H. Krim. Image denoising: A nonlinear robust statistical approach. IEEE Trans. Signal Processing, 49(12):3045-3054, 2001.
2. M.J. Black and A. Rangarajan. On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. International Journal of Computer Vision, 25(19):57-92, 1996.
3. V. Blanz and T. Vetter. A morphable model for the synthesis of 3D faces. In SIGGRAPH' 99 Conference Proceedings, pages 187-194, 1999.
4. A.B.A. Graf and F.A. Wichmann. Gender classification of human faces. In H.H. B̈ulthoff, S.-W. Lee, T.A. Poggio, and C. Wallraven, editors, Biologically Motivated Computer Vision 2002, LNCS 2525, pages 491-501, 2002.
5. O.L. Mangasarian. Machine learning via polyhedral concave minimization. Technical Report 95-20, Computer Sciences Department, University of Wisconsin, 1995.
6. S. Mika, B. Scḧolkopf, A.J. Smola, K.-R. M̈uller, M. Scholz, and G. R̈atsch. Kernel PCA and de-noising in feature spaces. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Advances in Neural Information Processing Systems, volume 11, pages 536- 542. MIT Press, 1999.
7. G. R̈atsch, B. Scḧolkopf, A.J. Smola, S. Mika, T. Onoda, and K.-R. M̈uller. Robust ensemble learning. In A.J. Smola, P.L. Bartlett, B. Scḧolkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 207-219. MIT Press, Cambridge, MA, 2000.
8. B. Scḧolkopf, A. Smola, R.C. Williamson, and P.L. Bartlett. New support vector algorithms. Neural Computation, 12:1207 - 1245, 2000. also NeuroCOLT Technical Report NC-TR-1998-031.
9. T. Takahashi and T. Kurita. Robust de-noising by kernel PCA. In J.R. Dorronsoro, editor, Artificial Neural Networks - ICANN 2002, LNCS 2415, pages 727-732. Springer Verlag, 2002.