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| 概要 |
We consider supervised image classification based on Markov Random Fields (MRFs), which are derived by the Jeffreys divergence between class-conditional probability densities. It is shown that the MRF... gives an extension of Switzer’s smoothing method for classification. Further, the exact error rate due to the MRF is obtained in a simple setup, and several properties of the classification are derived. Our procedure is applied to two multispectral images, and shows a good performance.続きを見る
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