Temporal stabilization of video object segmentation for 3D-TV applications

We present a method for improving the temporal stability of video object segmentation algorithms for 3D-TV applications. First, two quantitative measures to evaluate temporal stability without ground-truth are presented. Then, a pseudo-3D curve evolution method, which spatio-temporally stabilizes the estimated object segments is introduced. Temporal stability is achieved by re-distributing existing object segmentation errors such that they are less disturbing when the scene is rendered and viewed in 3D. Our starting point is the hypothesis that if making segmentation errors are inevitable, they should be made in a temporally consistent way for 3D TV applications. This hypothesis is supported by the experiments, which show that there is significant improvement in segmentation quality both in terms of the objective quantitative measures and in terms of the viewing comfort in subjective perceptual tests. This shows that it is possible to increase the object segmentation quality without increasing the actual segmentation accuracy.