Even though the edges of a texture image may have various orientations and their locations in the image may be random, for the magnitude of Fourier transform of the image, the contribution of all edges with the same orientation will be stacked up in the orientation being perpendicular to the edges. This special phenomenon is called as auto-registration of the magnitude spectrum. In this paper we propose a method to exploit the auto-registration property of the magnitude spectra for texture identification and image segmentation.
We propose a new Gibbs sampling algorithm, the soft- criterion acceptance algorithm, and use it for the texture synthesis. The new algorithm combines the advantages of the ICM algorithm in computations and of the algorithm of simulated annealing (SA) in global convergence. As a result, it is computationally efficient in comparison with the Gibbs sampler by S. Geman and D. Geman. The key idea is that the difference of the maximum and minimum of the energy functions is used to construct a soft criterion for updating each pixel value in a probabilistic acceptance fashion that is similar to the SA. The algorithm is verified by computational experiments.
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