Unsupervised image segmentation using a telegraph parameterization of Pickard random fields
This article presents a non-supervised segmentation method based upon a discrete-level unilateral Markov field model of the image. Such models have been shown to yield numerically efficient algorithms, for segmentation and for hyperparameter estimation as well. Our contribution lies in the derivation of a parsimonious telegraphic parameterization of the unilateral Markov field. On a theoretical level, this parameterization ensures that some important properties of the field (e.g., stationarity) do hold. On a practical level, it reduces the computational complexity of the algorithm used in the segmentation and parameter estimation stages of the procedure. In addition, it decreases the number of hyperparameters that must be estimated, thereby improving the convergence speed and accuracy of the corresponding estimation method.