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Combining local and global features for image segmentation using iterative classification and region merging

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2 Author(s)
Qiyao Yu ; Syst. Design Eng., Waterloo Univ., Ont., Canada ; Clausi, D.A.

In MRF based unsupervised segmentation, the MRF model parameters are typically estimated globally. Those global statistics sometimes are far from accurate for local areas if the image is highly non-stationary, and hence will generate false boundaries. The problem cannot be solved if local statistics are not considered. This work incorporates the local feature of edge strength in the MRF energy function, and segmentation is obtained by reducing the energy function using iterative classification and region merging.

Published in:

Computer and Robot Vision, 2005. Proceedings. The 2nd Canadian Conference on

Date of Conference:

9-11 May 2005

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