Scene segmentation from dense displacement vector fields using randomized Hough transform

Abstract

Abstract A method which combines two different paradigms for segmenting an image scene by evaluating dense displacement vector fields is presented. First, a rough but robust decomposition of the vector field is achieved by using randomized Hough transform, a technique which is independent from prior knowledge about the actual number of segments in the scene. Subsequently, a merging step fuses those segments most likely belonging to the same object. Finally, a refinement of the segmentation mask is attained by means of a maximum a posteriori (MAP) criterion. To this end the mask is modelled as a Gibbs-Markov random field under the assumption that the scene objects are spatially continuous and only moving slowly between consecutive image frames.