Markov Random Fields (MRFs) are used in computer vision as an effective method for reconstructing a function starting from a set of noisy, or sparse data, or in the integration of early vision processes to label physical discontinuities. The MRF formalism is attractive because it enables the assumptions used to be explicitly stated in the energy function. The drawbacks of such models have been the computational complexity of the implementation, and the difficulty in estimating the parameters of the model. \n In this thesis, the deterministic approximation to the MRF models derived by Girosi and Geiger  is investigated, and following that approach, a MIMD based algorithm is developed and implemented on a network of T800 transputers under the Trollius operating system. A serial version of the algorithm has also been implemented on a SUN 4 under Unix. \n The network of transputers is configured as a 2-dimensional mesh of processors (currently 16 configured as a $4 \times 4$ mesh), and the input partitioning method is used to distribute the original image across the network. \n The implementation of the algorithm is described, and the suitability of the transputer for image processing tasks is discussed. \n The algorithm was applied to a number of images for edge detection, and produced good results in a small number of iterations.
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