We address the problem of recognizing a configuration of a piece of garment fairly spread out on a flat surface. We suppose that the background surface is invariant and that its color is sufficiently dissimilar from the color of a piece of garment. This assumption enables quite reliable segmentation followed by extraction of the garment contour. The contour is approximated by a polygon which is then fitted to a polygonal garment model. The model is specific for each category of garment (e.g. towel, pants, shirt) and its parameters are learned from training data. The fitting procedure is based on minimization of the energy function expressing dissimilarities between observed and expected data. The fitted model provides reliable estimation of garment landmark points which can be utilized for an automated folding using a pair of robotic arms. The proposed method was experimentally verified on a dataset of images. It was also deployed to a robot and tested in a real-time automated folding.
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