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Content Provider | IEEE Xplore Digital Library |
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Author | Hu, Y. Dennis, T.J. |
Copyright Year | 1992 |
Description | Author affiliation: Essex Univ., Colchester, UK (Hu, Y.; Dennis, T.J.) |
Abstract | Proposes a selective update scheme for both SA (simulated annealing) and ICMs (iterated conditional modes) which only visits sites within inhomogeneous neighborhoods. A second scheme is proposed to enhance the update confidence at each site by incorporating contextual information in terms of neighbor label class probability distributions, instead of their current realizations. The two update schemes reduce the computation demand and improve estimation accuracy. Both schemes are tested on a noise-contaminated Markov random field test image. The results show that ICM and SA with selective update achieve a computational savings of five times on average, without introducing noticeable degradation. The confidence enhanced update scheme, working with SA and ICM, much improves the final estimation accuracy. In particular for ICM, it produces similar results to those of SA, but uses only a fraction of the iterations needed by the latter.< |
Starting Page | 257 |
Ending Page | 264 |
File Size | 454213 |
Page Count | 8 |
File Format | |
ISBN | 0818627425 |
DOI | 10.1109/CBMS.1992.244940 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 1992-06-14 |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Simulated annealing Image segmentation Stochastic processes Probability distribution Markov random fields Maximum a posteriori estimation Computational modeling Computer vision Image processing Strontium |
Content Type | Text |
Resource Type | Article |
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