A Probabilistic Method for Motion Pattern Segmentation

In this paper we present an approach for probabilistic motion pattern segmentation. We combine level-set methods for image segmentation with motion estimations based on probability distribution functions (pdf´s) calculated at each image position. To this end, we extend a region based level-set framework to exploit the motion pdf´s. We then compare segmentation results of the pdf-based with those of optical-flow-based motion segmentation approaches. We found that the straightforward way of characterizing the segmented region by spatially averaging the motion measurement pdf´s does not yield satisfactory results. However, describing the spatial characteristics of the motion pdf´s with nonparametric density estimates enables to solve complex motion segmentation problems. In particular for situations with demanding motion patterns like partly overlapping objects and transparent motion, we show that the probabilistic approach yields better results. This confirms the idea that for motion processing it is beneficial to consistently retain the uncertainty and ambiguity of the measurement process right up to the final integration stage, instead of directly processing optical flow vectors.