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This work deals with global statistical unsupervised segmentation algorithms. In the context of Magnetic Resonance Image (MRI), an accurate and robust segmentation can be achieved by combining both the Hidden Markov Random Field (HMRF) model and the Expectation-Maximization (EM) algorithm. This EM–HMRF approach is accomplished by taking into account spatial information to improve the segmentation...
The processing capabilities of wireless sensor nodes enable to aggregate redundant data to limit total data flow over the network. The main property of a good aggregation algorithm is to extract the most representative data by using minimum resources. From this point of view, sampling is a promising aggregation method, that acts as surrogate for the whole data, and once extracted can be used to answer...
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