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This paper presents a new approach to the use of Gibbs distributions (GD) for modeling and segmentation of noisy and textured images. Specifically, the paper presents random field models for noisy and textured image data based upon a hierarchy of GD. It then presents dynamic programming based segmentation algorithms for noisy and textured images,(More)
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 yields the a posteriori distribution of the scene value at each pixel, given the total noisy image, in a recursive way. The a posteriori distribution together(More)
We present a statistical method to detect regions whose apparent motion in the image is not conforming to the dominant motion of the background resulting from the camera movement. Alternatively, the same scheme can be used to track a particular region of interest of the scene. The apparent motion induced by the camera motion is represented by a 2D(More)
The valid parameter spaces of infiniteand finite-lattice (2-D noncausal) Gaussian Markov random fields (GMRF's) are investigated. For the infinite-lattice fields, the valid parameter space is shown to admit an explicit description; a procedure that yields the valid parameter space is presented. This procedure is then applied to the second-order(More)