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- W2005125065 abstract "ABSTRACTSyntheticAperture Radar data may contain useful infoimation about small structures like riversand men made structures. Using the Markov Random Field based segmentation algorithms, thatperform quite well on homogeneously textured areas (i.e., agriculture land cover), these structuresmay be lost if they are small (1-2 pixel wide). The merging of various sources of information at alow level of the Markov Random Field region label process, makes it possible to recover at leastpartly the fine structures in the SAR data. The data fusion makes use of Bayesian inference about the Markovian property of neighbourhood systems. This article shows that the proposed method is valid and technical feasible, based on extensive validations. Key words: Image segmentation, data fusion, Markov Random Fields, Bayesian Newworks, Synthetic Aperture Radar data.1. INTRODUCTION A typical implementation of a Markov Random Field (MRF) exploits an energy function toexpress the intuitive feeling that the processing of one pixel site depends not only on that very sitebut also on its context, or, more specifically, its neighbourhood. Although non-causal MRF-based" @default.
- W2005125065 created "2016-06-24" @default.
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- W2005125065 date "1996-12-17" @default.
- W2005125065 modified "2023-09-23" @default.
- W2005125065 title "<title>Data fusion in a Markov random-field-based image segmentation approach</title>" @default.
- W2005125065 doi "https://doi.org/10.1117/12.262877" @default.
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