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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 criterion, the initial label of wavelet coefficients...
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 characterized by a feature field and a label field...
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 overcome the shortcomings, the contents of image...
A statistical object detection and tracking mutual feedback scheme, combining Gaussian mixture model (GMM) based on principal component analysis (PCA) and expectation maximization (EM) Kalman filter algorithm, is proposed in this paper. In space object detection stage, PCA provides compact and decorrelated feature space, the tracked object feature is statistically represented as GMM in RGB color space,...
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