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RSRN: Rich Side-Output Residual Network for Medial Axis Detection
AbstractIn this paper, we propose a Rich Side-output Residual Network (RSRN) for medial axis detection for the ICCV 2017 workshop challenge on detecting symmetry in the wild. RSRN uses the rich features of fully convolutional network by hierarchically fusing side-outputs in a deep-to-shallow manner to decrease the residual between the detection result and the ground-truth, which refines the detection result hierarchically. Experimental results show that the proposed RSRN improve the performance compared with baseline on both SKLARGE and BMAX500 datasets.
Related Material
[pdf][bibtex]@InProceedings{Liu_2017_ICCV,
author = {Liu, Chang and Ke, Wei and Jiao, Jianbin and Ye, Qixiang},
title = {RSRN: Rich Side-Output Residual Network for Medial Axis Detection},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}
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