Efficient algorithm for finding the exact minimum barrier distance

TL;DR: This paper proposes an exact and iteration free solution on a minimum spanning tree that largely reduces the search space of shortest paths, resulting an efficient and high quality distance transform algorithm and introduces a boundary dissimilarity measure to compliment the shortage of distance transform for salient object detection.

TL;DR: The usefulness of surroundedness for eye fixation prediction by proposing a Boolean Map based Saliency model (BMS), based on a Gestalt principle of figure-ground segregation, which computes a saliency map by discovering surrounded regions via topological analysis of Boolean maps.

TL;DR: A novel MoNet model to deeply exploit motion cues for boosting video object segmentation performance from two aspects, i.e., frame representation learning and segmentation refinement, provides new state-of-the-art performance on three competitive benchmark datasets.

TL;DR: It is shown that skeletons can be produced by simple procedures and since these are based on Euclidean distances it is assumed that they are superior to skeletons based on d4−, d8−, and even octagonal metrics.

TL;DR: The relative merits of performing local operations on ~ digitized picture in parallel or sequentially are discussed and some applications of the connected component and distance functions are presented.