We investigated perceptual segmentation in the context of a perceived-orientation task. Stimuli were dot clusters formed by the union of a large elliptical sub-cluster and a secondary circular sub-cluster. We manipulated the separation between the two sub-clusters, their common dot density, and the size of the secondary sub-cluster. As the separation between sub-clusters increased, the orientation perceived by observers shifted gradually from the global principal axis of the entire cluster to that of the main sub-cluster alone. Thus, with increasing separation, the dots within the secondary sub-cluster were assigned systematically lower weights in the principal-axis computation. In addition, this shift occurred at smaller separations for higher dot densities-consistent with the idea that reliable segmentation is possible with smaller separations when the dot density is high. We propose that the visual system employs a robust statistical estimator in this task and that data points are weighted differentially based on the likelihood that they arose from a separate generative process. However, unlike in standard robust estimation, weights based on residuals are insufficient to characterize human segmentation. Rather, these must be computed based on more comprehensive generative models of dot clusters.