In this paper the well-known problem of reconstructing hv-convex polyominoes is considered from a set of noisy data. Differently from the usual approach of Binary Tomography, this leads to a probabilistic evaluation in the reconstruction algorithm, where different pixels assume different probabilities to be part of the reconstructed image. An iterative algorithm is then applied, which, starting from a random choice, leads to an explicit reconstruction matching the noisy data.
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We provide a quadratic-time algorithm for generating hv-convex polyominoes according to a given horizontal projection. The method can be used to generate hv-convex polyominoes with the prescribed projection and with a fixed or arbitrary horizontal dimension, from a uniform random distribution.
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