Issue No. 06 - June (1984 vol. 6)
Haluk Derin , Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003.
Howard Elliott , Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003.
Roberto Cristi , Department of Electrical and Computer Enginerring, University of Massachusetts, Amherst, MA 01003.; Department of Electrical Engineering, University of Michigan, Dearborn, MI 48128
Donald Geman , Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 01003.
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 with a criterion of optimality then determine a Bayes estimate of the scene. The algorithm presented is an extension of a 1-D Bayes smoothing algorithm to 2-D and it gives the optimum Bayes estimate for the scene value at each pixel. 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, a special class of Markov random fields, and the Bayes smoothing algorithm is applied on overlapping strips (horizontal/vertical) of the image consisting of several rows (columns). It is assumed that the signal (scene values) vector sequence along the strip is a vector Markov chain. Since signal correlation in one of the dimensions is not fully used along the edges of the strip, estimates are generated only along the middle sections of the strips. The overlapping strips are chosen such that the union of the middle sections of the strips gives the whole image. The Bayes smoothing algorithm presented here is valid for scene random fields consisting of multilevel (discrete) or continuous random variables.
D. Geman, H. Elliott, R. Cristi and H. Derin, "Bayes Smoothing Algorithms for Segmentation of Binary Images Modeled by Markov Random Fields," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 6, no. , pp. 707-720, 1984.