6 research outputs found
Automatic generation of dense non-rigid optical flow
There hardly exists any large-scale datasets with dense optical flow of
non-rigid motion from real-world imagery as of today. The reason lies mainly in
the difficulty of human annotation to generate optical flow ground-truth. To
circumvent the need for human annotation, we propose a framework to
automatically generate optical flow from real-world videos. The method extracts
and matches objects from video frames to compute initial constraints, and
applies a deformation over the objects of interest to obtain dense optical flow
fields. We propose several ways to augment the optical flow variations.
Extensive experimental results show that training on our automatically
generated optical flow outperforms methods that are trained on rigid synthetic
data using FlowNet-S, PWC-Net, and LiteFlowNet. Datasets and algorithms of our
optical flow generation framework is available at
https://github.com/lhoangan/arap_flow.Comment: The paper is under consideration at Computer Vision and Image
Understandin
Manipulation Strategies for the Rank Maximal Matching Problem
We consider manipulation strategies for the rank-maximal matching problem. In
the rank-maximal matching problem we are given a bipartite graph such that denotes a set of applicants and a set of posts. Each
applicant has a preference list over the set of his neighbours in
, possibly involving ties. Preference lists are represented by ranks on the
edges - an edge has rank , denoted as , if post
belongs to one of 's -th choices. A rank-maximal matching is one in which
the maximum number of applicants is matched to their rank one posts and subject
to this condition, the maximum number of applicants is matched to their rank
two posts, and so on. A rank-maximal matching can be computed in time, where denotes the number of applicants, the
number of edges and the maximum rank of an edge in an optimal solution.
A central authority matches applicants to posts. It does so using one of the
rank-maximal matchings. Since there may be more than one rank- maximal matching
of , we assume that the central authority chooses any one of them randomly.
Let be a manipulative applicant, who knows the preference lists of all
the other applicants and wants to falsify his preference list so that he has a
chance of getting better posts than if he were truthful. In the first problem
addressed in this paper the manipulative applicant wants to ensure that
he is never matched to any post worse than the most preferred among those of
rank greater than one and obtainable when he is truthful. In the second problem
the manipulator wants to construct such a preference list that the worst post
he can become matched to by the central authority is best possible or in other
words, wants to minimize the maximal rank of a post he can become matched
to