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We study several ad\u2010hoc labeling models for binary images consisting of nonoverlapping rectangular contiguous regions. Nesting and equivalence of these models are studied. We observed a noticeable increase in performance even in cases when the differences between the models were seemingly insignificant. We use the proposed models for segmentation of windowpanes and windows in orthographically rectified fa\u00e7ade images. Segmented window patches are always axis\u2010parallel nonoverlapping rectangles which must also be aligned in our strongest model. We show experimentally that even very weak data model in the MAP formulation of the optimal segmentation problem gives very good segmentation results. \u00a9 2009 Wiley Periodicals, Inc. 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