Segmenting Highly Textured Nonstationary Background

Detection of unusual objects amongst a highly textured background is a difficult problem, especially when the texture is manifest in the temporal dimension as well. Outdoor scenes involving waving trees or moving water are examples of such a scenario, but are nevertheless frequently encountered in real world vision applications. By defining a simple but rotationally sensitive Local Binary Pattern (LBP) operator and applying it in a probabilistic sense we present a compact but useful feature for tackling moving textures. But as we demonstrate, this alone is not sufficient for good segmentation in difficult circumstances. Cooccurrence of different features in a pixel’s local neighbourhood provides a powerful mechanism for boosting the reliability of the foreground/background decision task. By using the conditional probabilities yielded by pairwise cooccurrence of 4-connected pixels, and casting the problem as one of Combinatorial Optimization, our results show that useful segmentation is possible from challenging dynamic backgrounds.