Suxuan Bian

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In this paper, a multiscale image segmentation algorithm based on Markov random field and spatial context fuzzy clustering in wavelet domain is presented. At the determination of pixel label stage, the feature field of image is described by Gaussian mixture model, the label field of image is characterized by Markov random field, according to the Bayesian(More)
In this paper, an unsupervised image segmentation algorithm is proposed, which combines spatial constraints with a kernel fuzzy c-means (KFCM) clustering algorithm. Conventional KFCM clustering segmentation algorithm does not incorporate the spatial context information of image, which makes it sensitive to the noise and intensity variations. In order to(More)
In this paper an unsupervised image segmentation method is presented, which combines wavelet domain Markov random field (WD-MRF) with the modified fuzzy c-means (FCM) clustering algorithm. At the label establishment stage, a WD-MRF tree is employed to model the statistical properties of multiresolution wavelet coefficients. Each wavelet coefficient is(More)
In this paper, an unsupervised multiresolution image segmentation algorithm is put forward, which combines interscale and intrascale Markov random field and fuzzy c-means clustering with spatial constraints. In the initial label determination of wavelet coefficient phase, the statistical distribution property of wavelet coefficients is characterized by(More)
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