Optimal segmentation of signals and its application to image denoising and boundary feature extraction

@article{Han2004OptimalSO,
  title={Optimal segmentation of signals and its application to image denoising and boundary feature extraction},
  author={Tony X. Han and Steven Kay and Thomas S. Huang},
  journal={2004 International Conference on Image Processing, 2004. ICIP '04.},
  year={2004},
  volume={4},
  pages={2693-2696 Vol. 4}
}
An optimal procedure for segmenting one-dimensional signals whose parameters are unknown and change at unknown times is presented. The method is maximum likelihood segmentation, which is computed using dynamic programming. In this procedure, the number of segments of the signal need not be known a priori but is automatically chosen by the minimum description length rule. The signal is modeled as unknown DC levels and unknown jump instants with an example chosen to illustrate the procedure. This… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.
9 Citations
22 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 22 references

Fundamentals of Statistical Signal Processing

  • S. Kay
  • Volume 1: Estimation Theory, Prentice-Hall
  • 1993
Highly Influential
6 Excerpts

Conslructing simple sable descriptions for imagc partitioning

  • A. Benveniste
  • IJCV
  • 1998

Region competilion : Unifying snakes . region growing , and BnyeslMDL for multi - band image segmentation

  • S. Zhu, A. Yuille
  • IEEE Trans . PAMI , vo 1
  • 1997

and M

  • P. Prandoni, M. Goodwin
  • Vetterli, “Optimal Segmentation for Signal…
  • 1997
1 Excerpt

Similar Papers

Loading similar papers…