A Study of Supervised , Semi-supervised and Unsupervised Multiscale Bayesian Image Segmentation

@inproceedings{SongASO,
  title={A Study of Supervised , Semi-supervised and Unsupervised Multiscale Bayesian Image Segmentation},
  author={Xiaomu Song and Guoliang Fan}
}
In this paper, we study multiscale Bayesian image segmentation with respect to the different availability of image features. Specifically, wavelet domain hidden Markov models (HMMs) are adopted to obtain statistical image characterization. The joint multi-context and multi-scale (JMCMS) approach is also applied to exploit robust multiscale contextual information. We first review the supervised Bayesian segmentation algorithms where complete image features are given. Secondly, we study semi… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-7 of 7 references

Similar Papers

Loading similar papers…