TY - GEN
T1 - Contour guided hierarchical model for shape matching
AU - Su, Yuanqi
AU - Liu, Yuehu
AU - Cuan, Bonan
AU - Zheng, Nanning
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/2/17
Y1 - 2015/2/17
N2 - For its simplicity and effectiveness, star model is popular in shape matching. However, it suffers from the loose geometric connections among parts. In the paper, we present a novel algorithm that reconsiders these connections and reduces the global matching to a set of interrelated local matching. For the purpose, we divide the shape template into overlapped parts and model the matching through a part-based layered structure that uses the latent variable to constrain parts' deformation. As for inference, each part is used for localizing candidates by the partial matching. Thanks to the contour fragments, the partial matching can be solved via modified dynamic programming. The overlapped regions among parts of the template are then explored to make the candidates of parts meet at their shared points. The process is fulfilled via a refined procedure based on iterative dynamic programming. Results on ETHZ shape and Inria Horse datasets demonstrate the benefits of the proposed algorithm.
AB - For its simplicity and effectiveness, star model is popular in shape matching. However, it suffers from the loose geometric connections among parts. In the paper, we present a novel algorithm that reconsiders these connections and reduces the global matching to a set of interrelated local matching. For the purpose, we divide the shape template into overlapped parts and model the matching through a part-based layered structure that uses the latent variable to constrain parts' deformation. As for inference, each part is used for localizing candidates by the partial matching. Thanks to the contour fragments, the partial matching can be solved via modified dynamic programming. The overlapped regions among parts of the template are then explored to make the candidates of parts meet at their shared points. The process is fulfilled via a refined procedure based on iterative dynamic programming. Results on ETHZ shape and Inria Horse datasets demonstrate the benefits of the proposed algorithm.
UR - https://www.scopus.com/pages/publications/84973863246
U2 - 10.1109/ICCV.2015.188
DO - 10.1109/ICCV.2015.188
M3 - 会议稿件
AN - SCOPUS:84973863246
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 1609
EP - 1617
BT - 2015 International Conference on Computer Vision, ICCV 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Computer Vision, ICCV 2015
Y2 - 11 December 2015 through 18 December 2015
ER -