Hierarchical Regions for Image Segmentation

@inproceedings{Wesolkowski2004HierarchicalRF,
  title={Hierarchical Regions for Image Segmentation},
  author={Slawomir Wesolkowski and Paul W. Fieguth},
  booktitle={ICIAR},
  year={2004}
}
Image segmentation is one of the key problems in computer vision. Gibbs Random Fields (GRFs), which produce elegant models, but which have very poor computational speed have been widely applied to image segmentation. In this paper, we propose a hierarchical region-based approach to the GRF. In contrast to block-based hierarchies usually constructed for GRFs, the irregular region-based approach is a far more natural model in segmenting real images. By deliberately oversegmenting at the finer… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
Showing 1-3 of 3 extracted citations

A similarity measure between fuzzy regions to obtain a hierarchy of fuzzy image segmentations

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence) • 2008
View 4 Excerpts
Highly Influenced

IRGS: Image Segmentation Using Edge Penalties and Region Growing

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2008
View 3 Excerpts

References

Publications referenced by this paper.
Showing 1-10 of 11 references

and S

P. Fiegut
Wesolkowski, “Highlight and Shading Invariant Color Image Segmentation Using Simulated Annealing,” Energy Minimization Methods in Computer Vision and Pattern Recognition III, Sophia-Antipolis, France, September • 2001
View 2 Excerpts

Computer and Robot Vision, Vol

R. M. Haralick, L. G. Shapiro
1, AddisonWelsey, • 1992
View 1 Excerpt

Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

IEEE Transactions on Pattern Analysis and Machine Intelligence • 1984

Similar Papers

Loading similar papers…