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Object segmentation is a fundamental task in various computer vision applications. Although used extensively for object recognition, texture has lately been ignored as a feature used for background modelling and object segmentation. The complexity of working with texture descriptors for segmentation in videos is two-fold: the descriptive features cannot be calculated in real time and features extracted based on arbitrarily chosen regions or blocks in the frame are not stable enough to allow for building models sufficiently accurate, yet simple enough to be used for real-time segmentation. The paper proposes an approach that can be used to detect regions of texture, stable enough to be modelled using probabilistic models commonly used for foreground segmentation. Based on the evaluated stable texture regions, a discriminative texture descriptor is proposed that can be evaluated in real time. Features based on this descriptor are able to enhance the segmentation performance of segmentation algorithms on some very "hard" sequences. © 2009. The copyright of this document resides with its authors.