A statistical model for contours in images

@article{Destrempes2004ASM,
  title={A statistical model for contours in images},
  author={François Destrempes and Max Mignotte},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2004},
  volume={26},
  pages={626-638}
}
In this paper, we describe a statistical model for the gradient vector field of the gray level in images validated by different experiments. Moreover, we present a global constrained Markov model for contours in images that uses this statistical model for the likelihood. Our model is amenable to an iterative conditional estimation (ICE) procedure for the estimation of the parameters; our model also allows segmentation by means of the simulated annealing (SA) algorithm, the iterated conditional… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 38 REFERENCES

Statistical Edge Detection: Learning and Evaluating Edge Cues

  • IEEE Trans. Pattern Anal. Mach. Intell.
  • 2003
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Détection Non-Supervisée de Contours et Localisation de Formes à l’Aide de Modèles Statistiques

F. Destrempes
  • Master Thesis, Université de Montréal, Apr. 2002.
  • 2002
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

On optimal linear filtering for edge detection

  • IEEE Trans. Image Processing
  • 2002
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Edge Detector Evaluation Using Empirical ROC Curves

  • Computer Vision and Image Understanding
  • 2001
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

JetStream: probabilistic contour extraction with particles

  • Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001
  • 2001
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Global and Local Methods of Unsupervised Bayesian Segmentation of Images

B. Braathen, P. Masson, W. Pieczynski
  • Machine Graphics and Vision, vol. 2, no. 1, pp. 39-52, 1993.
  • 1993
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Unsupervised texture segmentation using a statistical wavelet-based hierarchical multidata model

  • Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)
  • 2003
VIEW 1 EXCERPT

Image Segmentation by Data-Driven Markov Chain Monte Carlo

  • IEEE Trans. Pattern Anal. Mach. Intell.
  • 2002
VIEW 1 EXCERPT