  Automatic segmentation of objects from a single image is a challenging
problem which generally requires training on large number of images. We
consider the problem of automatically segmenting only the dynamic objects from
a given pair of images of a scene captured from different positions. We exploit
dense correspondences along with saliency measures in order to first localize
the interest points on the dynamic objects from the two images. We propose a
novel approach based on techniques from computational geometry in order to
automatically segment the dynamic objects from both the images using a top-down
segmentation strategy. We discuss how the proposed approach is unique in
novelty compared to other state-of-the-art segmentation algorithms. We show
that the proposed approach for segmentation is efficient in handling large
motions and is able to achieve very good segmentation of the objects for
different scenes. We analyse the results with respect to the manually marked
ground truth segmentation masks created using our own dataset and provide key
observations in order to improve the work in future.
