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A combined Markov random field and wave-packet transform-based approach for image segmentation

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1 Author(s)
Bello, M.G. ; Charles Stark Draper Lab. Inc., Cambridge, MA, USA

The author formulates a novel segmentation algorithm which combines the use of Markov random field models for image-modeling with the use of the discrete wavepacket transform for image analysis. Image segmentations are derived and refined at a sequence of resolution levels, using as data selected wave-packet transform images or “channels”. The segmentation algorithm is compared with nonmultiresolution Markov random field-based image segmentation algorithms in the context of synthetic image example problems, and found to be both significantly more efficient and effective

Published in:

Image Processing, IEEE Transactions on  (Volume:3 ,  Issue: 6 )