Skeletonization Combined with Deep Neural Networks for Superpixel Temporal Propagation

Medial axis representation (a.k.a. shape skeleton) seems to be present in visual processing, but its relevance has remained unclear. Here, we show the potentials of the medial axis transformation in the temporal propagation of superpixels. We combine (i) state-of-the-art deep neural network ‘sensors’ for optical flow and for depth estimation and (ii) a superpixel algorithm with (iii) the medial axis transformation to obtain frame-to-frame propagation of visual objects. We study the precision of this deep learning facilitated superpixel temporal propagation. We discuss the advantages of the method compared to the temporal propagation of the superpixels themselves.