In this section
@article{Chang:2018:10.1109/TPAMI.2017.2777486,
author = {Chang, HJ and Fischer, T and Petit, M and Zambelli, M and Demiris, Y},
doi = {10.1109/TPAMI.2017.2777486},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
pages = {2920--2934},
title = {Learning kinematic structure correspondences using multi-order similarities},
url = {http://dx.doi.org/10.1109/TPAMI.2017.2777486},
volume = {40},
year = {2018}
}
TY - JOUR
AB - We present a novel framework for finding the kinematic structure correspondences between two articulated objects in videos via hypergraph matching. In contrast to appearance and graph alignment based matching methods, which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Thus our method allows matching the structure of objects which have similar topologies or motions, or a combination of the two. Our main contributions are summarised as follows: (i)casting the kinematic structure correspondence problem into a hypergraph matching problem by incorporating multi-order similarities with normalising weights, (ii)introducing a structural topology similarity measure by aggregating topology constrained subgraph isomorphisms, (iii)measuring kinematic correlations between pairwise nodes, and (iv)proposing a combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on synthetic and real data, showing that various other recent and state of the art methods are outperformed. Our method is not limited to a specific application nor sensor, and can be used as building block in applications such as action recognition, human motion retargeting to robots, and articulated object manipulation.
AU - Chang,HJ
AU - Fischer,T
AU - Petit,M
AU - Zambelli,M
AU - Demiris,Y
DO - 10.1109/TPAMI.2017.2777486
EP - 2934
PY - 2018///
SN - 0162-8828
SP - 2920
TI - Learning kinematic structure correspondences using multi-order similarities
T2 - IEEE Transactions on Pattern Analysis and Machine Intelligence
UR - http://dx.doi.org/10.1109/TPAMI.2017.2777486
UR - http://ieeexplore.ieee.org/document/8119820
UR - http://hdl.handle.net/10044/1/54198
VL - 40
ER -
