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Parallel and deterministic algorithms from MRFs: Surface reconstruction and integration

  • Davi Geiger
  • Federico Girosi
Stereo And Reconstruction
Part of the Lecture Notes in Computer Science book series (LNCS, volume 427)

Keywords

Partition Function Energy Function Surface Reconstruction Sparse Data Deterministic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. [1]
    M. Bertero, T. Poggio, and V. Torre. Ill-posed problems in early vision. Technical report. Also Proc. IEEE, in press.Google Scholar
  2. [2]
    A. Blake and A. Zisserman. Visual Reconstruction. MIT Press, Cambridge, Mass, 1987.Google Scholar
  3. [3]
    P. B. Chou and C. M. Brown. Multimodal reconstruction and segmentation with Markov random fields and HCF optimization. In Proceedings Image Understanding Workshop, pages 214–221, Cambridge, MA, February 1988. Morgan Kaufmann, San Mateo, CA.Google Scholar
  4. [4]
    E. Gamble, D. Geiger, T. Poggio, and D. Weinshall. Integration of vision modules and labeling of surface discontinuities. Invited paper to IEEE Trans. Sustems, Man & Cybernetics, December 1989.Google Scholar
  5. [5]
    E. B. Gamble and T. Poggio. Visual integration and detection of discontinuities: The key role of intensity edges. A.I. Memo No. 970, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, October 1987.Google Scholar
  6. [6]
    D. Geiger and F. Girosi. Parallel and deterministic algorithms for mrfs: surface reconstruction and integration. A.I. Memo No. 1114, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, May 1989.Google Scholar
  7. [7]
    D. Geiger and T. Poggio. An optimal scale for edge detection. In Proceedings IJCAI, August 1987.Google Scholar
  8. [8]
    D. Geiger and A. Yuille. A common framework for image segmentation and surface reconstruction. Harvard Robotics Laboratory Technical Report 89-7, Harvard, August 1989.Google Scholar
  9. [9]
    Davi Geiger. Visual models with statistical field theory. PhD thesis, Massachusetts Institute of Technology, 1989.Google Scholar
  10. [10]
    S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6:721–741, 1984.Google Scholar
  11. [11]
    S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi. Optimization by simulated annealing. Science, 220:219–227, 1983.Google Scholar
  12. [12]
    C. Koch, J. Marroquin, and A. Yuille. Analog ‘neuronal’ networks in early vision. Proc. Natl. Acad. Sci., 83:4263–4267, 1985.Google Scholar
  13. [13]
    J. L. Marroquin. Deterministic Bayesian estimation of Markovian random fields with applications to computational vision. In Proceedings of the International Conference on Computer Vision, London, England, June 1987. IEEE, Washington, DC.Google Scholar
  14. [14]
    J. L. marroquin, S. Mitter, and T. Poggio. Probabilistic solution of ill-posed problems in computational vision. In L. Baumann, editor, Proceedings Image Understanding Workshop, pages 293–309, McLean, VA, August 1985. Scientific Applications International Corporation.Google Scholar
  15. [15]
    N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller. Equation of state calculations by fast computing machines. J. Phys. Chem, 21:1087, 1953.Google Scholar
  16. [16]
    A. N. Tikhonov and V. Y. Arsenin. Solutions of Ill-posed Problems. W.H.Winston, Washington, D.C., 1977.Google Scholar
  17. [17]
    G. Wahba. Practical approximate solutions to linear operator equations when the data are noisy. SIAM J. Numer. Anal., 14, 1977.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Davi Geiger
    • 1
    • 2
  • Federico Girosi
    • 1
  1. 1.Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyUSA
  2. 2.Siemens Corporate Research, IncPrinceton

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