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• W2135492096 abstract "A new image segmentation algorithm is presented, based on recursive Bayes smoothing of images modeled by Markov random fields and corrupted by independent additive noise. The Bayes smoothing algorithm presented is an extension of a 1-D algorithm to 2-D and it yields the a posteriori distribution and the optimum Bayes estimate of the scene value at each pixel, using the total noisy image data. Computational concerns in 2-D, however, necessitate certain simplifying assumptions on the model and approximations on the implementation of the algorithm. In particular, the scene (noiseless image) is modeled as a Markov mesh random field and the algorithm is applied on (horizontal/vertical) strips of the image. The Bayes smoothing algorithm is applied to segmentation of two level test images and remotely sensed SAR data obtained from SEASAT, yielding remarkably good segmentation results even for very low signal to noise ratios." @default.
• W2135492096 created "2016-06-24" @default.
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• W2135492096 date "2005-03-24" @default.
• W2135492096 modified "2023-09-26" @default.
• W2135492096 title "Bayes smoothing algorithms for segmentation of images modeled by Markov random fields" @default.
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• W2135492096 doi "https://doi.org/10.1109/icassp.1984.1172642" @default.
• W2135492096 hasPublicationYear "2005" @default.
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